Estimating danger from future falling cargo

ABSTRACT

A method for estimating a future fall of a cargo, the method may include receiving by a computerized system, sensed information related to driving sessions of multiple vehicles; applying a machine learning process on the sensed information to detect actual or estimated cargo falling events and generate one or more future falling cargo predictors for multiple types of cargo; estimating, from the sensed information, an impact of cargo falling events related to at least some of the types of cargo; and responding to the estimating, wherein the responding comprises at least one out of (a) storing the one or more future falling cargo predictors for the multiple types of cargo, (b) transmitting the one or more future falling cargo predictors for the multiple types of cargo; (c) storing the estimated impact of cargo falling events related to the at least some of the types of cargo, and (d) transmitting the impact of cargo falling events related to the at least some of the types of cargo.

CROSS REFERENCE

The application is a continuation of PCT patent application PCT/IB2019058208 filing date Sep. 27, 2019 which claims priority from U.S. provisional Ser. No. 62/747,147 filing date Oct. 18, 2018. This application claims priority from U.S. provisional patent Ser. No. 62/827,112 filing date Mar. 31, 2019 both incorporated herein by reference.

TECHNICAL FIELD

The present disclosure generally relates to detecting and avoiding obstacles in an autonomous driving environment.

BACKGROUND

Assisted and autonomous driving systems are known in the art. In such systems, computer implemented systems control (at least to some extent) some, or all, of a vehicle's driving functions, e.g., speed, telemetry, braking, etc. The vehicle is typically equipped with one or more sensors, e.g., a camera, to provide the system with current information regarding the driving environment. The current information for the driving environment is typically used by the driving system to determine how to drive on roadways according to road maps stored on the vehicle.

SUMMARY

A method, system and non-transitory computer readable medium ford etecting fallen cargo.

BRIEF DESCRIPTION OF THE DRAWINGS

The embodiments of the disclosure will be understood and appreciated more fully from the following detailed description, taken in conjunction with the drawings in which:

FIG. 1A illustrates an example of a method;

FIG. 1B illustrates an example of a signature;

FIG. 1C illustrates an example of a dimension expansion process;

FIG. 1D illustrates an example of a merge operation;

FIG. 1E illustrates an example of hybrid process;

FIG. 1F illustrates an example of a first iteration of the dimension expansion process;

FIG. 1G illustrates an example of a method;

FIG. 1H illustrates an example of a method;

FIG. 1I illustrates an example of a method;

FIG. 1J illustrates an example of a method;

FIG. 1K illustrates an example of a method;

FIG. 1L illustrates an example of a method;

FIG. 1M illustrates an example of a method;

FIG. 1N illustrates an example of a matching process and a generation of a higher accuracy shape information;

FIG. 1O illustrates an example of an image and image identifiers;

FIG. 1P illustrates an example of an image, approximated regions of interest, compressed shape information and image identifiers;

FIG. 1Q illustrates an example of an image, approximated regions of interest, compressed shape information and image identifiers;

FIG. 1R illustrates an example of an image;

FIG. 1S illustrates an example of a method;

FIG. 2A illustrates an example of images of different scales;

FIG. 2B illustrates an example of images of different scales;

FIG. 2C illustrates an example of a method;

FIG. 2D illustrates an example of a method;

FIG. 2E illustrates an example of a method;

FIG. 2F illustrates an example of a method;

FIG. 2G illustrates an example of different images;

FIG. 2H illustrates an example of a method;

FIG. 2I illustrates an example of a method;

FIG. 2J illustrates an example of a method;

FIG. 2K illustrates an example of different images acquisition angles;

FIG. 2L illustrates an example of a method;

FIG. 2M illustrates an example of a method;

FIG. 2N illustrates an example of a system;

FIG. 3A is a partly-pictorial, partly-block diagram illustration of an exemplary obstacle detection and mapping system, constructed and operative in accordance with embodiments described herein;

FIG. 3B is a block diagram of an exemplary autonomous driving system to be integrated in the vehicle of FIG. 3A;

FIG. 3C is a flowchart of an exemplary process to be performed by the autonomous driving system of FIG. 3B;

FIG. 4 is a block diagram of an exemplary obstacle avoidance server of FIG. 3A;

FIG. 5 is a flowchart of an exemplary process to be performed by the obstacle avoidance server of FIG. 4;

FIG. 6 is an example of a method;

FIG. 7 is an example of a method;

FIG. 8 is an example of a driving scenario;

FIG. 9 is an example of a driving scenario;

FIG. 10 is an example of a driving scenario;

FIG. 11 is an example of a method;

FIG. 12 is an example of a method;

FIG. 13 is an example of a method;

FIG. 14 is an example of a driving scenario;

FIG. 15 is an example of a driving scenario;

FIG. 16 is an example of a scene;

FIG. 17 is an example of a scene;

FIG. 18 is an example of a driving scenario;

FIG. 19 is an example of a method;

FIG. 20 is an example of a method;

FIG. 21 is an example of a method;

FIG. 22 is an example of a driving scenario;

FIG. 23 is an example of a driving scenario;

FIG. 24 is an example of a driving scenario;

FIG. 25 is an example of a driving scenario;

FIG. 26 is an example of a driving scenario;

FIG. 27 is an example of a driving scenario;

FIG. 28 is an example of a method;

FIG. 29 is an example of entity movement functions;

FIG. 30 is an example of a method;

FIG. 31 is an example of a method;

FIG. 32 is an example of a method;

FIG. 33 is an example of a method;

FIG. 34 is an example of a method;

FIG. 35 is an example of a method;

FIG. 36 is an example of a driving scenario;

FIG. 37 is an example of a method;

FIG. 38 is an example of a method; and

FIGS. 39-44 illustrate various data structures including a concept, test images and matching results, as well as various processes related to the data structures;

FIG. 45 is an example of a method;

FIGS. 46-59 illustrate various fallen cargo and driving patterns;

FIGS. 60-61 are examples of methods; and

FIG. 62 is an example of an image.

DESCRIPTION OF EXAMPLE EMBODIMENTS

The specification and/or drawings may refer to an image. An image is an example of a media unit. Any reference to an image may be applied mutatis mutandis to a media unit. A media unit may be an example of sensed information. Any reference to a media unit may be applied mutatis mutandis to any type of natural signal such as but not limited to signal generated by nature, signal representing human behavior, signal representing operations related to the stock market, a medical signal, financial series, geodetic signals, geophysical, chemical, molecular, textual and numerical signals, time series, and the like. Any reference to a media unit may be applied mutatis mutandis to sensed information. The sensed information may be of any kind and may be sensed by any type of sensors —such as a visual light camera, an audio sensor, a sensor that may sense infrared, radar imagery, ultrasound, electro-optics, radiography, LIDAR (light detection and ranging), etc. The sensing may include generating samples (for example, pixel, audio signals) that represent the signal that was transmitted, or otherwise reach the sensor.

The specification and/or drawings may refer to a spanning element. A spanning element may be implemented in software or hardware. Different spanning element of a certain iteration are configured to apply different mathematical functions on the input they receive. Non-limiting examples of the mathematical functions include filtering, although other functions may be applied.

The specification and/or drawings may refer to a concept structure. A concept structure may include one or more clusters. Each cluster may include signatures and related metadata. Each reference to one or more clusters may be applicable to a reference to a concept structure.

The specification and/or drawings may refer to a processor. The processor may be a processing circuitry. The processing circuitry may be implemented as a central processing unit (CPU), and/or one or more other integrated circuits such as application-specific integrated circuits (ASICs), field programmable gate arrays (FPGAs), full-custom integrated circuits, etc., or a combination of such integrated circuits.

Any combination of any steps of any method illustrated in the specification and/or drawings may be provided.

Any combination of any subject matter of any of claims may be provided.

Any combinations of systems, units, components, processors, sensors, illustrated in the specification and/or drawings may be provided.

Low Power Generation of Signatures

The analysis of content of a media unit may be executed by generating a signature of the media unit and by comparing the signature to reference signatures. The reference signatures may be arranged in one or more concept structures or may be arranged in any other manner. The signatures may be used for object detection or for any other use.

The signature may be generated by creating a multidimensional representation of the media unit. The multidimensional representation of the media unit may have a very large number of dimensions. The high number of dimensions may guarantee that the multidimensional representation of different media units that include different objects is sparse—and that object identifiers of different objects are distant from each other—thus improving the robustness of the signatures.

The generation of the signature is executed in an iterative manner that includes multiple iterations, each iteration may include an expansion operations that is followed by a merge operation. The expansion operation of an iteration is performed by spanning elements of that iteration. By determining, per iteration, which spanning elements (of that iteration) are relevant—and reducing the power consumption of irrelevant spanning elements—a significant amount of power may be saved.

In many cases, most of the spanning elements of an iteration are irrelevant—thus after determining (by the spanning elements) their relevancy—the spanning elements that are deemed to be irrelevant may be shut down a/or enter an idle mode.

FIG. 1A illustrates a method 5000 for generating a signature of a media unit.

Method 5000 may start by step 5010 of receiving or generating sensed information.

The sensed information may be a media unit of multiple objects.

Step 5010 may be followed by processing the media unit by performing multiple iterations, wherein at least some of the multiple iterations comprises applying, by spanning elements of the iteration, dimension expansion process that are followed by a merge operation.

The processing may include:

Step 5020 of performing a k′th iteration expansion process (k may be a variable that is used to track the number of iterations).

Step 5030 of performing a k′th iteration merge process.

Step 5040 of changing the value of k.

Step 5050 of checking if all required iterations were done—if so proceeding to step 5060 of completing the generation of the signature. Else—jumping to step 5020.

The output of step 5020 is a k′th iteration expansion results 5120.

The output of step 5030 is a k′th iteration merge results 5130.

For each iteration (except the first iteration)—the merge result of the previous iteration is an input to the current iteration expansion process.

At least some of the K iterations involve selectively reducing the power consumption of some spanning elements (during step 5020) that are deemed to be irrelevant.

FIG. 1B is an example of an image signature 6027 of a media unit that is an image 6000 and of an outcome 6013 of the last (K′th) iteration.

The image 6001 is virtually segments to segments 6000(i,k). The segments may be of the same shape and size but this is not necessarily so.

Outcome 6013 may be a tensor that includes a vector of values per each segment of the media unit. One or more objects may appear in a certain segment. For each object—an object identifier (of the signature) points to locations of significant values, within a certain vector associated with the certain segment.

For example—a top left segment (6001(1,1)) of the image may be represented in the outcome 6013 by a vector V(1,1) 6017(1,1) that has multiple values. The number of values per vector may exceed 100, 200, 500, 1000, and the like.

The significant values (for example—more than 10, 20, 30, 40 values, and/or more than 0.1%, 0.2%. 0.5%, 1%, 5% of all values of the vector and the like) may be selected. The significant values may have the values—but may be selected in any other manner.

FIG. 1B illustrates a set of significant responses 6015(1,1) of vector V(1,1) 6017(1,1). The set includes five significant values (such as first significant value SV1(1,1) 6013(1,1,1), second significant value SV2(1,1), third significant value SV3(1,1), fourth significant value SV4(1,1), and fifth significant value SV5(1,1) 6013(1,1,5).

The image signature 6027 includes five indexes for the retrieval of the five significant values—first till fifth identifiers ID1-ID5 are indexes for retrieving the first till fifth significant values.

FIG. 1C illustrates a k′th iteration expansion process.

The k′th iteration expansion process start by receiving the merge results 5060′ of a previous iteration.

The merge results of a previous iteration may include values are indicative of previous expansion processes—for example—may include values that are indicative of relevant spanning elements from a previous expansion operation, values indicative of relevant regions of interest in a multidimensional representation of the merge results of a previous iteration.

The merge results (of the previous iteration) are fed to spanning elements such as spanning elements 5061(1)-5061(J).

Each spanning element is associated with a unique set of values. The set may include one or more values. The spanning elements apply different functions that may be orthogonal to each other. Using non-orthogonal functions may increase the number of spanning elements—but this increment may be tolerable.

The spanning elements may apply functions that are decorrelated to each other—even if not orthogonal to each other.

The spanning elements may be associated with different combinations of object identifiers that may “cover” multiple possible media units. Candidates for combinations of object identifiers may be selected in various manners—for example based on their occurrence in various images (such as test images) randomly, pseudo randomly, according to some rules and the like. Out of these candidates the combinations may be selected to be decorrelated, to cover said multiple possible media units and/or in a manner that certain objects are mapped to the same spanning elements.

Each spanning element compares the values of the merge results to the unique set (associated with the spanning element) and if there is a match—then the spanning element is deemed to be relevant. If so—the spanning element completes the expansion operation.

If there is no match—the spanning element is deemed to be irrelevant and enters a low power mode. The low power mode may also be referred to as an idle mode, a standby mode, and the like. The low power mode is termed low power because the power consumption of an irrelevant spanning element is lower than the power consumption of a relevant spanning element.

In FIG. 1C various spanning elements are relevant (5061(1)-5061(3)) and one spanning element is irrelevant (5061(J)).

Each relevant spanning element may perform a spanning operation that includes assigning an output value that is indicative of an identity of the relevant spanning elements of the iteration. The output value may also be indicative of identities of previous relevant spanning elements (from previous iterations).

For example—assuming that spanning element number fifty is relevant and is associated with a unique set of values of eight and four—then the output value may reflect the numbers fifty, four and eight—for example one thousand multiplied by (fifty+forty) plus forty. Any other mapping function may be applied.

FIG. 1C also illustrates the steps executed by each spanning element:

Checking if the merge results are relevant to the spanning element (step 5091).

If-so—completing the spanning operation (step 5093).

If not—entering an idle state (step 5092).

FIG. 1D is an example of various merge operations.

A merge operation may include finding regions of interest. The regions of interest are regions within a multidimensional representation of the sensed information. A region of interest may exhibit a more significant response (for example a stronger, higher intensity response).

The merge operation (executed during a k′th iteration merge operation) may include at least one of the following:

Step 5031 of searching for overlaps between regions of interest (of the k′th iteration expansion operation results) and define regions of interest that are related to the overlaps.

Step 5032 of determining to drop one or more region of interest, and dropping according to the determination.

Step 5033 of searching for relationships between regions of interest (of the k′th iteration expansion operation results) and define regions of interest that are related to the relationship.

Step 5034 of searching for proximate regions of interest (of the k′th iteration expansion operation results) and define regions of interest that are related to the proximity. Proximate may be a distance that is a certain fraction (for example less than 1%) of the multi-dimensional space, may be a certain fraction of at least one of the regions of interest that are tested for proximity.

Step 5035 of searching for relationships between regions of interest (of the k′th iteration expansion operation results) and define regions of interest that are related to the relationship.

Step 5036 of merging and/or dropping k′th iteration regions of interest based on shape information related to shape of the k′th iteration regions of interest.

The same merge operations may applied in different iterations.

Alternatively, different merge operations may be executed during different iterations.

FIG. 1E illustrates an example of a hybrid process and an input image 6001.

The hybrid process is hybrid in the sense that some expansion and merge operations are executed by a convolutional neural network (CNN) and some expansion and merge operations (denoted additional iterations of expansion and merge) are not executed by the CNN— but rather by a process that may include determining a relevancy of spanning elements and entering irrelevant spanning elements to a low power mode.

In FIG. 1E one or more initial iterations are executed by first and second CNN layers 6010(1) and 6010(2) that apply first and second functions 6015(1) and 6015(2).

The output of these layers provided information about image properties. The image properties may not amount to object detection. Image properties may include location of edges, properties of curves, and the like.

The CNN may include additional layers (for example third till N'th layer 6010(N)) that may provide a CNN output 6018 that may include object detection information. It should be noted that the additional layers may not be included.

It should be noted that executing the entire signature generation process by a hardware CNN of fixed connectivity may have a higher power consumption—as the CNN will not be able to reduce the power consumption of irrelevant nodes.

FIG. 1F illustrates an input image 6001, and a single iteration of an expansion operation and a merge operation.

In FIG. 1F the input image 6001 undergoes two expansion operations.

The first expansion operation involves filtering the input image by a first filtering operation 6031 to provide first regions of interest (denoted 1) in a first filtered image 6031′.

The first expansion operation also involves filtering the input image by a second filtering operation 6032 to provide first regions of interest (denoted 2) in a second filtered image 6032′,

The merge operation includes merging the two images by overlaying the first filtered image on the second filtered image to provide regions of interest 1, 2, 12 and 21. Region of interest 12 is an overlap area shared by a certain region of interest 1 and a certain region of interest 2. Region of interest 21 is a union of another region of interest 1 and another region of interest 2.

FIG. 1G illustrates method 5200 for generating a signature.

Method 5200 may include the following sequence of steps:

Step 5210 of receiving or generating an image.

Step 5220 of performing a first iteration expansion operation (which is an expansion operation that is executed during a first iteration)

Step 5230 of performing a first iteration merge operation.

Step 5240 of amending index k (k is an iteration counter). In FIG. 7 in incremented by one—this is only an example of how the number of iterations are tracked.

Step 5260 of performing a k′th iteration expansion operation on the (k−1)′th iteration merge results.

Step 5270 of performing a k′th iteration merge operation (on the k′th iteration expansion operation results.

Step 5280 of changing the value of index k.

Step 5290 of checking if all iteration ended (k reached its final value—for example K).

If no—there are still iterations to be executed—jumping from step 5290 to step 5260.

If yes—jumping to step 5060 of completing the generation of the signature. This may include, for example, selecting significant attributes, determining retrieval information (for example indexes) that point to the selected significant attributes.

Step 5220 may include:

Step 5222 of generating multiple representations of the image within a multi-dimensional space of f(1) dimensions. The expansion operation of step 5220 generates a first iteration multidimensional representation of the first image. The number of dimensions of this first iteration multidimensional representation is denoted f(1).

Step S224 of assigning a unique index for each region of interest within the multiple representations. For example, referring to FIG. 6—indexes 1 and indexes 2 are assigned to regions of interests generated during the first iteration expansion operations 6031 and 6032.

Step 5230 may include:

Step 5232 of searching for relationships between regions of interest and define regions of interest that are related to the relationships. For example—union or intersection illustrate din FIG. 6.

Step 5234 of assigning a unique index for each region of interest within the multiple representations. For example—referring to FIG. 6—indexes 1, 2, 12 and 21.

Step 5260 may include:

Step 5262 of generating multiple representations of the merge results of the (k−1)′th iteration within a multi-dimensional space of f(k) dimensions. The expansion operation of step 5260 generates a k′th iteration multidimensional representation of the first image. The number of dimensions of this kth iteration multidimensional representation is denoted f(k).

Step 5264 of assigning a unique index for each region of interest within the multiple representations.

Step 5270 may include

Step 5272 of searching for relationships between regions of interest and define regions of interest that are related to the relationships.

Step 5274 of Assigning a unique index for each region of interest within the multiple representations.

FIG. 1H illustrates a method 5201. In method 5201 the relationships between the regions of interest are overlaps.

Thus—step 5232 is replaced by step S232′ of searching for overlaps between regions of interest and define regions of interest that are related to the overlaps.

Step 5272 is replaced by step 5272′ of searching for overlaps between regions of interest and define regions of interest that are related to the overlaps.

FIG. 1I illustrates a method 7000 for low-power calculation of a signature.

Method 7000 starts by step 7010 of receiving or generating a media unit of multiple objects.

Step 7010 may be followed by step 7012 of processing the media unit by performing multiple iterations, wherein at least some of the multiple iterations comprises applying, by spanning elements of the iteration, dimension expansion process that are followed by a merge operation.

The applying of the dimension expansion process of an iteration may include (a) determining a relevancy of the spanning elements of the iteration; and (b) completing the dimension expansion process by relevant spanning elements of the iteration and reducing a power consumption of irrelevant spanning elements until, at least, a completion of the applying of the dimension expansion process.

The identifiers may be retrieval information for retrieving the significant portions.

The at least some of the multiple iterations may be a majority of the multiple iterations.

The output of the multiple iteration may include multiple property attributes for each segment out of multiple segments of the media unit; and wherein the significant portions of an output of the multiple iterations may include more impactful property attributes.

The first iteration of the multiple iteration may include applying the dimension expansion process by applying different filters on the media unit.

The at least some of the multiple iteration exclude at least a first iteration of the multiple iterations. See, for example, FIG. 1E.

The determining the relevancy of the spanning elements of the iteration may be based on at least some identities of relevant spanning elements of at least one previous iteration.

The determining the relevancy of the spanning elements of the iteration may be based on at least some identities of relevant spanning elements of at least one previous iteration that preceded the iteration.

The determining the relevancy of the spanning elements of the iteration may be based on properties of the media unit.

The determining the relevancy of the spanning elements of the iteration may be performed by the spanning elements of the iteration.

Method 7000 may include a neural network processing operation that may be executed by one or more layers of a neural network and does not belong to the at least some of the multiple iterations. See, for example, FIG. 1E.

The at least one iteration may be executed without reducing power consumption of irrelevant neurons of the one or more layers.

The one or more layers may output information about properties of the media unit, wherein the information differs from a recognition of the multiple objects.

The applying, by spanning elements of an iteration that differs from the first iteration, the dimension expansion process may include assigning output values that may be indicative of an identity of the relevant spanning elements of the iteration. See, for example, FIG. 1C.

The applying, by spanning elements of an iteration that differs from the first iteration, the dimension expansion process may include assigning output values that may be indicative a history of dimension expansion processes until the iteration that differs from the first iteration.

The each spanning element may be associated with a subset of reference identifiers. The determining of the relevancy of each spanning elements of the iteration may be based a relationship between the subset of the reference identifiers of the spanning element and an output of a last merge operation before the iteration.

The output of a dimension expansion process of an iteration may be a multidimensional representation of the media unit that may include media unit regions of interest that may be associated with one or more expansion processes that generated the regions of interest.

The merge operation of the iteration may include selecting a subgroup of media unit regions of interest based on a spatial relationship between the subgroup of multidimensional regions of interest. See, for example, FIGS. 3 and 6.

Method 7000 may include applying a merge function on the subgroup of multidimensional regions of interest. See, for example, FIGS. 1C and 1F.

Method 7000 may include applying an intersection function on the subgroup of multidimensional regions of interest. See, for example, FIGS. 1C and 1F.

The merge operation of the iteration may be based on an actual size of one or more multidimensional regions of interest.

The merge operation of the iteration may be based on relationship between sizes of the multidimensional regions of interest. For example—larger multidimensional regions of interest may be maintained while smaller multidimensional regions of interest may be ignored of.

The merge operation of the iteration may be based on changes of the media unit regions of interest during at least the iteration and one or more previous iteration.

Step 7012 may be followed by step 7014 of determining identifiers that are associated with significant portions of an output of the multiple iterations.

Step 7014 may be followed by step 7016 of providing a signature that comprises the identifiers and represents the multiple objects.

Localization and Segmentation

Any of the mentioned above signature generation method provides a signature that does not explicitly includes accurate shape information. This adds to the robustness of the signature to shape related inaccuracies or to other shape related parameters.

The signature includes identifiers for identifying media regions of interest.

Each media region of interest may represent an object (for example a vehicle, a pedestrian, a road element, a human made structure, wearables, shoes, a natural element such as a tree, the sky, the sun, and the like) or a part of an object (for example—in the case of the pedestrian—a neck, a head, an arm, a leg, a thigh, a hip, a foot, an upper arm, a forearm, a wrist, and a hand). It should be noted that for object detection purposes a part of an object may be regarded as an object.

The exact shape of the object may be of interest.

FIG. 1J illustrates method 7002 of generating a hybrid representation of a media unit.

Method 7002 may include a sequence of steps 7020, 7022, 7024 and 7026.

Step 7020 may include receiving or generating the media unit.

Step 7022 may include processing the media unit by performing multiple iterations, wherein at least some of the multiple iterations comprises applying, by spanning elements of the iteration, dimension expansion process that are followed by a merge operation.

Step 7024 may include selecting, based on an output of the multiple iterations, media unit regions of interest that contributed to the output of the multiple iterations.

Step 7026 may include providing a hybrid representation, wherein the hybrid representation may include (a) shape information regarding shapes of the media unit regions of interest, and (b) a media unit signature that includes identifiers that identify the media unit regions of interest.

Step 7024 may include selecting the media regions of interest per segment out of multiple segments of the media unit. See, for example, FIG. 2.

Step 7026 may include step 7027 of generating the shape information.

The shape information may include polygons that represent shapes that substantially bound the media unit regions of interest. These polygons may be of a high degree.

In order to save storage space, the method may include step 7028 of compressing the shape information of the media unit to provide compressed shape information of the media unit.

FIG. 1K illustrates method 5002 for generating a hybrid representation of a media unit.

Method 5002 may start by step 5011 of receiving or generating a media unit.

Step 5011 may be followed by processing the media unit by performing multiple iterations, wherein at least some of the multiple iterations comprises applying, by spanning elements of the iteration, dimension expansion process that are followed by a merge operation.

The processing may be followed by steps 5060 and 5062.

The processing may include steps 5020, 5030, 5040 and 5050.

Step 5020 may include performing a k′th iteration expansion process (k may be a variable that is used to track the number of iterations).

Step 5030 may include performing a k′th iteration merge process.

Step 5040 may include changing the value of k.

Step 5050 may include checking if all required iterations were done—if so proceeding to steps 5060 and 5062. Else—jumping to step 5020.

The output of step 5020 is a k′th iteration expansion result.

The output of step 5030 is a k′th iteration merge result.

For each iteration (except the first iteration)—the merge result of the previous iteration is an input to the current iteration expansion process.

Step 5060 may include completing the generation of the signature.

Step 5062 may include generating shape information regarding shapes of media unit regions of interest. The signature and the shape information provide a hybrid representation of the media unit.

The combination of steps 5060 and 5062 amounts to a providing a hybrid representation, wherein the hybrid representation may include (a) shape information regarding shapes of the media unit regions of interest, and (b) a media unit signature that includes identifiers that identify the media unit regions of interest.

FIG. 1L illustrates method 5203 for generating a hybrid representation of an image.

Method 5200 may include the following sequence of steps:

Step 5210 of receiving or generating an image.

Step 5230 of performing a first iteration expansion operation (which is an expansion operation that is executed during a first iteration)

Step 5240 of performing a first iteration merge operation.

Step 5240 of amending index k (k is an iteration counter). In FIG. 1L in incremented by one—this is only an example of how the number of iterations are tracked.

Step 5260 of performing a k′th iteration expansion operation on the (k−1)′th iteration merge results.

Step 5270 of Performing a k′th iteration merge operation (on the k′th iteration expansion operation results.

Step 5280 of changing the value of index k.

Step 5290 of checking if all iteration ended (k reached its final value—for example K).

If no—there are still iterations to be executed—jumping from step 5290 to step 5260.

If yes—jumping to step 5060.

Step 5060 may include completing the generation of the signature. This may include, for example, selecting significant attributes, determining retrieval information (for example indexes) that point to the selected significant attributes.

Step 5062 may include generating shape information regarding shapes of media unit regions of interest. The signature and the shape information provide a hybrid representation of the media unit.

The combination of steps 5060 and 5062 amounts to a providing a hybrid representation, wherein the hybrid representation may include (a) shape information regarding shapes of the media unit regions of interest, and (b) a media unit signature that includes identifiers that identify the media unit regions of interest.

Step 5220 may include:

Step 5222 of generating multiple representations of the image within a multi-dimensional space of f(k) dimensions.

Step S224 of assigning a unique index for each region of interest within the multiple representations. (for example, referring to FIG. 1F—indexes 1 and indexes 2 following first iteration expansion operations 6031 and 6032.

Step 5230 may include

Step 5226 of searching for relationships between regions of interest and define regions of interest that are related to the relationships. For example—union or intersection illustrated in FIG. 1F.

Step 5228 of assigning a unique index for each region of interest within the multiple representations. For example—referring to FIG. 1F—indexes 1, 2, 12 and 21.

Step 5260 may include:

Step 5262 of generating multiple representations of the merge results of the (k−1)′th iteration within a multi-dimensional space of f(k) dimensions. The expansion operation of step 5260 generates a k′th iteration multidimensional representation of the first image. The number of dimensions of this kth iteration multidimensional representation is denoted f(k).

Step 5264 of assigning a unique index for each region of interest within the multiple representations.

Step 5270 may include

Step 5272 of searching for relationships between regions of interest and define regions of interest that are related to the relationships.

Step 5274 of assigning a unique index for each region of interest within the multiple representations.

FIG. 1M illustrates method 5205 for generating a hybrid representation of an image.

Method 5200 may include the following sequence of steps:

Step 5210 of receiving or generating an image.

Step 5230 of performing a first iteration expansion operation (which is an expansion operation that is executed during a first iteration)

Step 5240 of performing a first iteration merge operation.

Step 5240 of amending index k (k is an iteration counter). In FIG. 1M in incremented by one—this is only an example of how the number of iterations are tracked.

Step 5260 of performing a k′th iteration expansion operation on the (k−1)′th iteration merge results.

Step 5270 of performing a k′th iteration merge operation (on the k′th iteration expansion operation results.

Step 5280 of changing the value of index k.

Step 5290 of checking if all iteration ended (k reached its final value—for example K).

If no—there are still iterations to be executed—jumping from step 5290 to step 5260.

If yes—jumping to steps 5060 and 5062.

Step 5060 may include completing the generation of the signature. This may include, for example, selecting significant attributes, determining retrieval information (for example indexes) that point to the selected significant attributes.

Step 5062 may include generating shape information regarding shapes of media unit regions of interest. The signature and the shape information provide a hybrid representation of the media unit.

The combination of steps 5060 and 5062 amounts to a providing a hybrid representation, wherein the hybrid representation may include (a) shape information regarding shapes of the media unit regions of interest, and (b) a media unit signature that includes identifiers that identify the media unit regions of interest.

Step 5220 may include:

Step 5221 of filtering the image with multiple filters that are orthogonal to each other to provide multiple filtered images that are representations of the image in a multi-dimensional space of f(1) dimensions. The expansion operation of step 5220 generates a first iteration multidimensional representation of the first image. The number of filters is denoted f(1).

Step S224 of assigning a unique index for each region of interest within the multiple representations. (for example, referring to FIG. 1F—indexes 1 and indexes 2 following first iteration expansion operations 6031 and 6032.

Step 5230 may include

Step 5226 of searching for relationships between regions of interest and define regions of interest that are related to the relationships. For example—union or intersection illustrated in FIG. 1F.

Step 5228 of assigning a unique index for each region of interest within the multiple representations. For example—referring to FIG. 1F—indexes 1, 2, 12 and 21.

Step 5260 may include:

Step 5262 of generating multiple representations of the merge results of the (k−1)′th iteration within a multi-dimensional space of f(k) dimensions. The expansion operation of step 5260 generates a k′th iteration multidimensional representation of the first image. The number of dimensions of this kth iteration multidimensional representation is denoted f(k).

Step 5264 of assigning a unique index for each region of interest within the multiple representations.

Step 5270 may include

Step 5272 of searching for relationships between regions of interest and define regions of interest that are related to the relationships.

Step 5274 of assigning a unique index for each region of interest within the multiple representations.

The filters may be orthogonal may be non-orthogonal—for example be decorrelated. Using non-orthogonal filters may increase the number of filters—but this increment may be tolerable.

Object Detection Using Compressed Shape Information

Object detection may include comparing a signature of an input image to signatures of one or more cluster structures in order to find one or more cluster structures that include one or more matching signatures that match the signature of the input image.

The number of input images that are compared to the cluster structures may well exceed the number of signatures of the cluster structures. For example—thousands, tens of thousands, hundreds of thousands (and even more) of input signature may be compared to much less cluster structure signatures. The ratio between the number of input images to the aggregate number of signatures of all the cluster structures may exceed ten, one hundred, one thousand, and the like.

In order to save computational resources, the shape information of the input images may be compressed.

On the other hand—the shape information of signatures that belong to the cluster structures may be uncompressed—and of higher accuracy than those of the compressed shape information.

When the higher quality is not required—the shape information of the cluster signature may also be compressed.

Compression of the shape information of cluster signatures may be based on a priority of the cluster signature, a popularity of matches to the cluster signatures, and the like.

The shape information related to an input image that matches one or more of the cluster structures may be calculated based on shape information related to matching signatures.

For example—a shape information regarding a certain identifier within the signature of the input image may be determined based on shape information related to the certain identifiers within the matching signatures.

Any operation on the shape information related to the certain identifiers within the matching signatures may be applied in order to determine the (higher accuracy) shape information of a region of interest of the input image identified by the certain identifier.

For example—the shapes may be virtually overlaid on each other and the population per pixel may define the shape.

For example—only pixels that appear in at least a majority of the overlaid shaped should be regarded as belonging to the region of interest.

Other operations may include smoothing the overlaid shapes, selecting pixels that appear in all overlaid shapes.

The compressed shape information may be ignored of or be taken into account.

FIG. 1N illustrates method 7003 of determining shape information of a region of interest of a media unit.

Method 7003 may include a sequence of steps 7030, 7032 and 7034.

Step 7030 may include receiving or generating a hybrid representation of a media unit. The hybrid representation includes compressed shape information.

Step 7032 may include comparing the media unit signature of the media unit to signatures of multiple concept structures to find a matching concept structure that has at least one matching signature that matches to the media unit signature.

Step 7034 may include calculating higher accuracy shape information that is related to regions of interest of the media unit, wherein the higher accuracy shape information is of higher accuracy than the compressed shape information of the media unit, wherein the calculating is based on shape information associated with at least some of the matching signatures.

Step 7034 may include at least one out of:

Determining shapes of the media unit regions of interest using the higher accuracy shape information.

For each media unit region of interest, virtually overlaying shapes of corresponding media units of interest of at least some of the matching signatures.

FIG. 1O illustrates a matching process and a generation of a higher accuracy shape information.

It is assumed that there are multiple (M) cluster structures 4974(1)-4974(M). Each cluster structure includes cluster signatures, metadata regarding the cluster signatures, and shape information regarding the regions of interest identified by identifiers of the cluster signatures.

For example—first cluster structure 4974(1) includes multiple (N1) signatures (referred to as cluster signatures CS) CS(1,1)-CS(1,N1) 4975(1,1)-4975(1,N1), metadata 4976(1), and shape information (Shapeinfo 4977(1)) regarding shapes of regions of interest associated with identifiers of the CSs.

Yet for another example—M′th cluster structure 4974(M) includes multiple (N2) signatures (referred to as cluster signatures CS) CS(M,1)-CS(M,N2) 4975(M,1)-4975(M,N2), metadata 4976(M), and shape information (Shapeinfo 4977(M)) regarding shapes of regions of interest associated with identifiers of the CSs.

The number of signatures per concept structure may change over time—for example due to cluster reduction attempts during which a CS is removed from the structure to provide a reduced cluster structure, the reduced structure is checked to determine that the reduced cluster signature may still identify objects that were associated with the (non-reduced) cluster signature—and if so the signature may be reduced from the cluster signature.

The signatures of each cluster structures are associated to each other, wherein the association may be based on similarity of signatures and/or based on association between metadata of the signatures.

Assuming that each cluster structure is associated with a unique object—then objects of a media unit may be identified by finding cluster structures that are associated with said objects. The finding of the matching cluster structures may include comparing a signature of the media unit to signatures of the cluster structures- and searching for one or more matching signature out of the cluster signatures.

In FIG. 1O—a media unit having a hybrid representation undergoes object detection. The hybrid representation includes media unit signature 4972 and compressed shape information 4973.

The media unit signature 4972 is compared to the signatures of the M cluster structures —from CS(1,1) 4975(1,1) till CS(M,N2) 4975(M,N2).

We assume that one or more cluster structures are matching cluster structures.

Once the matching cluster structures are found the method proceeds by generating shape information that is of higher accuracy then the compressed shape information.

The generation of the shape information is done per identifier.

For each j that ranges between 1 and J (J is the number of identifiers per the media unit signature 4972) the method may perform the steps of:

Find (step 4978(j)) the shape information of the j′th identifier of each matching signature- or of each signature of the matching cluster structure.

Generate (step 4979(j)) a higher accuracy shape information of the j′th identifier.

For example—assuming that the matching signatures include CS(1,1) 2975(1,1), CS(2,5) 2975(2,5), CS(7,3) 2975(7,3) and CS(15,2) 2975(15,2), and that the j′th identifier is included in CS(1,1) 2975(1,1), CS(7,3) 2975(7,3) and CS(15,2) 2975(15,2)—then the shape information of the j′th identifier of the media unit is determined based on the shape information associated with CS(1,1) 2975(1,1), CS(7,3) 2975(7,3) and CS(15,2) 2975(15,2).

FIG. 1P illustrates an image 8000 that includes four regions of interest 8001, 8002, 8003 and 8004. The signature 8010 of image 8000 includes various identifiers including ID1 8011, ID2 8012, ID3 8013 and ID4 8014 that identify the four regions of interest 8001, 8002, 8003 and 8004.

The shapes of the four regions of interest 8001, 8002, 8003 and 8004 are four polygons. Accurate shape information regarding the shapes of these regions of interest may be generated during the generation of signature 8010.

FIG. 1Q illustrates the compressing of the shape information to represent a compressed shape information that reflects simpler approximations (8001′, 8002′, 8003′ and 8004′) of the regions of interest 8001, 8002, 8003 and 8004. In this example simpler may include less facets, fewer values of angles, and the like.

The hybrid representation of the media unit, after compression represent an media unit with simplified regions of interest 8001′, 8002′, 8003′ and 8004′—as shown in FIG. 1R.

Scale Based Bootstrap

Objects may appear in an image at different scales. Scale invariant object detection may improve the reliability and repeatability of the object detection and may also use fewer number of cluster structures—thus reduced memory resources and also lower computational resources required to maintain fewer cluster structures.

FIG. 1S illustrates method 8020 for scale invariant object detection.

Method 8020 may include a first sequence of steps that may include step 8022, 8024, 8026 and 8028.

Step 8022 may include receiving or generating a first image in which an object appears in a first scale and a second image in which the object appears in a second scale that differs from the first scale.

Step 8024 may include generating a first image signature and a second image signature.

The first image signature includes a first group of at least one certain first image identifier that identifies at least a part of the object. See, for example image 8000′ of FIG. 2A. The person is identified by identifiers ID6 8016 and ID8 8018 that represent regions of interest 8006 and 8008.

The second image signature includes a second group of certain second image identifiers that identify different parts of the object.

See, for example image 8000 of FIG. 19. The person is identified by identifiers ID1 8011, ID2 8012, ID3 8013, and ID4 8014 that represent regions of interest 8001, 8002, 8003 and 8004.

The second group is larger than first group—as the second group has more members than the first group.

Step 8026 may include linking between the at least one certain first image identifier and the certain second image identifiers.

Step 8026 may include linking between the first image signature, the second image signature and the object.

Step 8026 may include adding the first signature and the second signature to a certain concept structure that is associated with the object. For example, referring to FIG. 1O, the signatures of the first and second images may be included in a cluster concept out of 4974(1)-4974(M).

Step 8028 may include determining whether an input image includes the object based, at least in part, on the linking. The input image differs from the first and second images.

The determining may include determining that the input image includes the object when a signature of the input image includes the at least one certain first image identifier or the certain second image identifiers.

The determining may include determining that the input image includes the object when the signature of the input image includes only a part of the at least one certain first image identifier or only a part of the certain second image identifiers.

The linking may be performed for more than two images in which the object appears in more than two scales.

For example, see FIG. 2B in which a person appears at three different scales—at three different images.

In first image 8051 the person is included in a single region of interest 8061 and the signature 8051′ of first image 8051 includes an identifier ID61 that identifies the single region of interest—identifies the person.

In second image 8052 the upper part of the person is included in region of interest 8068, the lower part of the person is included in region of interest 8069 and the signature 8052′ of second image 8052 includes identifiers ID68 and ID69 that identify regions of interest 8068 and 8069 respectively.

In third image 8053 the eyes of the person are included in region of interest 8062, the mouth of the person is included in region of interest 8063, the head of the person appears in region of interest 8064, the neck and arms of the person appear in region of interest 8065, the middle part of the person appears in region of interest 8066, and the lower part of the person appears in region of interest 8067. Signature 8053′ of third image 8053 includes identifiers ID62, ID63, ID64, ID65, ID55 and ID67 that identify regions of interest 8062-8067 respectively.

Method 8020 may link signatures 8051′, 8052′ and 8053′ to each other. For example—these signatures may be included in the same cluster structure.

Method 8020 may link (i) ID61, (ii) signatures ID68 and ID69, and (ii) signature ID62, ID63, ID64, ID65, ID66 and ID67.

FIG. 2C illustrates method 8030 for object detection.

Method 8030 may include the steps of method 8020 or may be preceded by steps 8022, 8024 and 8026.

Method 8030 may include a sequence of steps 8032, 8034, 8036 and 8038.

Step 8032 may include receiving or generating an input image.

Step 8034 may include generating a signature of the input image.

Step 8036 may include comparing the signature of the input image to signatures of a certain concept structure. The certain concept structure may be generated by method 8020.

Step 8038 may include determining that the input image comprises the object when at least one of the signatures of the certain concept structure matches the signature of the input image.

FIG. 2D illustrates method 8040 for object detection.

Method 8040 may include the steps of method 8020 or may be preceded by steps 8022, 8024 and 8026.

Method 8040 may include a sequence of steps 8041, 8043, 8045, 8047 and 8049.

Step 8041 may include receiving or generating an input image.

Step 8043 may include generating a signature of the input image, the signature of the input image comprises only some of the certain second image identifiers; wherein the input image of the second scale.

Step 8045 may include changing a scale of the input image to the first scale to a provide an amended input image.

Step 8047 may include generating a signature of the amended input image.

Step 8049 may include verifying that the input image comprises the object when the signature of the amended input image comprises the at least one certain first image identifier.

FIG. 2E illustrates method 8050 for object detection.

Method 8050 may include the steps of method 8020 or may be preceded by steps 8022, 8024 and 8026.

Method 8050 may include a sequence of steps 8052, 8054, 8056 and 8058.

Step 8052 may include receiving or generating an input image.

Step 8054 may include generating a signature of the input image.

Step 8056 may include searching in the signature of the input image for at least one of (a) the at least one certain first image identifier, and (b) the certain second image identifiers.

Step 8058 may include determining that the input image comprises the object when the signature of the input image comprises the at least one of (a) the at least one certain first image identifier, and (b) the certain second image identifiers.

It should be noted that step 8056 may include searching in the signature of the input image for at least one of (a) one or more certain first image identifier of the at least one certain first image identifier, and (b) at least one certain second image identifier of the certain second image identifiers.

It should be noted that step 8058 may include determining that the input image includes the object when the signature of the input image comprises the at least one of (a) one or more certain first image identifier of the at least one certain first image identifier, and (b) the at least one certain second image identifier.

Movement Based Bootstrapping

A single object may include multiple parts that are identified by different identifiers of a signature of the image. In cases such as unsupervised learning, it may be beneficial to link the multiple object parts to each other without receiving prior knowledge regarding their inclusion in the object.

Additionally or alternatively, the linking can be done in order to verify a previous linking between the multiple object parts.

FIG. 2F illustrates method 8070 for object detection.

Method 8070 is for movement based object detection.

Method 8070 may include a sequence of steps 8071, 8073, 8075, 8077, 8078 and 8079.

Step 8071 may include receiving or generating a video stream that includes a sequence of images.

Step 8073 may include generating image signatures of the images. Each image is associated with an image signature that comprises identifiers. Each identifier identifiers a region of interest within the image.

Step 8075 may include generating movement information indicative of movements of the regions of interest within consecutive images of the sequence of images. Step 8075 may be preceded by or may include generating or receiving location information indicative of a location of each region of interest within each image. The generating of the movement information is based on the location information.

Step 8077 may include searching, based on the movement information, for a first group of regions of interest that follow a first movement. Different first regions of interest are associated with different parts of an object.

Step 8078 may include linking between first identifiers that identify the first group of regions of interest.

Step 8079 may include linking between first image signatures that include the first linked identifiers.

Step 8079 may include adding the first image signatures to a first concept structure, the first concept structure is associated with the first image.

Step 8079 may be followed by determining whether an input image includes the object based, at least in part, on the linking

An example of various steps of method 8070 is illustrated in FIG. 2H.

FIG. 2G illustrates three images 8091, 8092 and 8093 that were taken at different points in time.

First image 8091 illustrates a gate 8089′ that is located in region of interest 8089 and a person that faces the gate. Various parts of the person are located within regions of interest 8081, 8082, 8083, 8084 and 8085.

The first image signature 8091′ includes identifiers ID81, ID82, ID83, ID84, ID85 and ID89 that identify regions of interest 8081, 8082, 8083, 8084, 8085 and 8089 respectively.

The first image location information 8091″ includes the locations L81, L82, L83, L84, L85 and L89 of regions of interest 8081, 8082, 8083, 8084, 8085 and 8089 respectively. A location of a region of interest may include a location of the center of the region of interest, the location of the borders of the region of interest or any location information that may define the location of the region of interest or a part of the region of interest.

Second image 8092 illustrates a gate that is located in region of interest 8089 and a person that faces the gate. Various parts of the person are located within regions of interest 8081, 8082, 8083, 8084 and 8085. Second image also includes a pole that is located within region of interest 8086. In the first image that the pole was concealed by the person.

The second image signature 8092′ includes identifiers ID81, ID82, ID83, ID84, ID85, ID86, and ID89 that identify regions of interest 8081, 8082, 8083, 8084, 8085, 8086 and 8089 respectively.

The second image location information 8092″ includes the locations L81, L82, L83, L84, L85, L86 and L89 of regions of interest 8081, 8082, 8083, 8084, 8085, 8086 and 8089 respectively.

Third image 8093 illustrates a gate that is located in region of interest 8089 and a person that faces the gate. Various parts of the person are located within regions of interest 8081, 8082, 8083, 8084 and 8085. Third image also includes a pole that is located within region of interest 8086, and a balloon that is located within region of interest 8087.

The third image signature 8093′ includes identifiers ID81, ID82, ID83, ID84, ID85, ID86, ID87 and ID89 that identify regions of interest 8081, 8082, 8083, 8084, 8085, 8086, 8087 and 8089 respectively.

The third image location information 8093″ includes the locations L81, L82, L83, L84, L85, L86, L87 and L89 of regions of interest 8081, 8082, 8083, 8084, 8085, 8086, 8086 and 8089 respectively.

The motion of the various regions of interest may be calculated by comparing the location information related to different images. The movement information may take into account the different in the acquisition time of the images.

The comparison shows that regions of interest 8081, 8082, 8083, 8084, 8085 move together and thus they should be linked to each other—and it may be assumed that they all belong to the same object.

FIG. 2H illustrates method 8100 for object detection.

Method 8100 may include the steps of method 8070 or may be preceded by steps 8071, 8073, 8075, 8077 and 8078.

Method 8100 may include the following sequence of steps:

Step 8102 of receiving or generating an input image.

Step 8104 of generating a signature of the input image.

Step 8106 of comparing the signature of the input image to signatures of a first concept structure. The first concept structure includes first identifiers that were linked to each other based on movements of first regions of interest that are identified by the first identifiers.

Step 8108 of determining that the input image includes a first object when at least one of the signatures of the first concept structure matches the signature of the input image.

FIG. 2I illustrates method 8110 for object detection.

Method 8110 may include the steps of method 8070 or may be preceded by steps 8071, 8073, 8075, 8077 and 8078.

Method 8110 may include the following sequence of steps:

Step 8112 of receiving or generating an input image.

Step 8114 of generating a signature of the input image.

Step 8116 of searching in the signature of the input image for at least one of the first identifiers.

Step 8118 of determining that the input image comprises the object when the signature of the input image comprises at least one of the first identifiers.

Object Detection that is Robust to Angle of Acquisition

Object detection may benefit from being robust to the angle of acquisition—to the angle between the optical axis of an image sensor and a certain part of the object. This allows the detection process to be more reliable, use fewer different clusters (may not require multiple clusters for identifying the same object from different images).

FIG. 2J illustrates method 8120 that includes the following steps:

Step 8122 of receiving or generating images of objects taken from different angles.

Step 8124 of finding images of objects taken from different angles that are close to each other. Close enough may be less than 1, 5, 10, 15 and 20 degrees—but the closeness may be better reflected by the reception of substantially the same signature.

Step 8126 of linking between the images of similar signatures. This may include searching for local similarities. The similarities are local in the sense that they are calculated per a subset of signatures. For example—assuming that the similarity is determined per two images—then a first signature may be linked to a second signature that is similar to the first image. A third signature may be linked to the second image based on the similarity between the second and third signatures- and even regardless of the relationship between the first and third signatures.

Step 8126 may include generating a concept data structure that includes the similar signatures.

This so-called local or sliding window approach, in addition to the acquisition of enough images (that will statistically provide a large angular coverage) will enable to generate a concept structure that include signatures of an object taken at multiple directions.

FIG. 2K illustrates a person 8130 that is imaged from different angles (8131, 8132, 8133, 8134, 8135 and 8136). While the signature of a front view of the person (obtained from angle 8131) differs from the signature of the side view of the person (obtained from angle 8136), the signature of images taken from multiple angles between angles 8141 and 8136 compensates for the difference—as the difference between images obtained from close angles are similar (local similarity) to each other.

Signature Tailored Matching Threshold

Object detection may be implemented by (a) receiving or generating concept structures that include signatures of media units and related metadata, (b) receiving a new media unit, generating a new media unit signature, and (c) comparing the new media unit signature to the concept signatures of the concept structures.

The comparison may include comparing new media unit signature identifiers (identifiers of objects that appear in the new media unit) to concept signature identifiers and determining, based on a signature matching criteria whether the new media unit signature matches a concept signature. If such a match is found then the new media unit is regarded as including the object associated with that concept structure.

It was found that by applying an adjustable signature matching criteria, the matching process may be highly effective and may adapt itself to the statistics of appearance of identifiers in different scenarios. For example—a match may be obtained when a relatively rear but highly distinguishing identifier appears in the new media unit signature and in a cluster signature, but a mismatch may be declared when multiple common and slightly distinguishing identifiers appear in the new media unit signature and in a cluster signature.

FIG. 2L illustrates method 8200 for object detection.

Method 8200 may include:

Step 8210 of receiving an input image.

Step 8212 of generating a signature of the input image.

Step 8214 of comparing the signature of the input image to signatures of a concept structure.

Step 8216 of determining whether the signature of the input image matches any of the signatures of the concept structure based on signature matching criteria, wherein each signature of the concept structure is associated within a signature matching criterion that is determined based on an object detection parameter of the signature.

Step 8218 of concluding that the input image comprises an object associated with the concept structure based on an outcome of the determining.

The signature matching criteria may be a minimal number of matching identifiers that indicate of a match. For example—assuming a signature that include few tens of identifiers, the minimal number may vary between a single identifier to all of the identifiers of the signature.

It should be noted that an input image may include multiple objects and that an signature of the input image may match multiple cluster structures. Method 8200 is applicable to all of the matching processes- and that the signature matching criteria may be set for each signature of each cluster structure.

Step 8210 may be preceded by step 8202 of determining each signature matching criterion by evaluating object detection capabilities of the signature under different signature matching criteria.

Step 8202 may include:

Step 8203 of receiving or generating signatures of a group of test images.

Step 8204 of calculating the object detection capability of the signature, for each signature matching criterion of the different signature matching criteria.

Step 8206 of selecting the signature matching criterion based on the object detection capabilities of the signature under the different signature matching criteria.

The object detection capability may reflect a percent of signatures of the group of test images that match the signature.

The selecting of the signature matching criterion comprises selecting the signature matching criterion that once applied results in a percent of signatures of the group of test images that match the signature that is closets to a predefined desired percent of signatures of the group of test images that match the signature.

The object detection capability may reflect a significant change in the percent of signatures of the group of test images that match the signature. For example—assuming, that the signature matching criteria is a minimal number of matching identifiers and that changing the value of the minimal numbers may change the percentage of matching test images. A substantial change in the percentage (for example a change of more than 10, 20, 30, 40 percent) may be indicative of the desired value. The desired value may be set before the substantial change, proximate to the substantial change, and the like.

For example, referring to FIG. 1O, cluster signatures CS(1,1), CS(2,5), CS(7,3) and CS(15,2) match unit signature 4972. Each of these matches may apply a unique signature matching criterion.

FIG. 2M illustrates method 8220 for object detection.

Method 8220 is for managing a concept structure.

Method 8220 may include:

Step 8222 of determining to add a new signature to the concept structure. The concept structure may already include at least one old signature. The new signature includes identifiers that identify at least parts of objects.

Step 8224 of determining a new signature matching criterion that is based on one or more of the identifiers of the new signature. The new signature matching criterion determines when another signature matches the new signature. The determining of the new signature matching criterion may include evaluating object detection capabilities of the signature under different signature matching criteria.

Step 8224 may include steps 8203, 8204 and 8206 (include din step 8206) of method 8200.

Examples of Systems

FIG. 2N illustrates an example of a system capable of executing one or more of the mentioned above methods.

The system include various components, elements and/or units.

A component element and/or unit may be a processing circuitry may be implemented as a central processing unit (CPU), and/or one or more other integrated circuits such as application-specific integrated circuits (ASICs), field programmable gate arrays (FPGAs), full-custom integrated circuits, etc., or a combination of such integrated circuits.

Alternatively, each component element and/or unit may implemented in hardware, firmware, or software that may be executed by a processing circuitry.

System 4900 may include sensing unit 4902, communication unit 4904, input 4911, processor 4950, and output 4919. The communication unit 4904 may include the input and/or the output.

Input and/or output may be any suitable communications component such as a network interface card, universal serial bus (USB) port, disk reader, modem or transceiver that may be operative to use protocols such as are known in the art to communicate either directly, or indirectly, with other elements of the system.

Processor 4950 may include at least some out of

Multiple spanning elements 4951(q).

Multiple merge elements 4952(r).

Object detector 4953.

Cluster manager 4954.

Controller 4955.

Selection unit 4956.

Object detection determination unit 4957.

Signature generator 4958.

Movement information unit 4959.

Identifier unit 4960.

There may be provided a method for low-power calculation of a signature, the method may include receiving or generating a media unit of multiple objects; processing the media unit by performing multiple iterations, wherein at least some of the multiple iterations may include applying, by spanning elements of the iteration, dimension expansion process that may be followed by a merge operation; wherein the applying of the dimension expansion process of an iteration may include determining a relevancy of the spanning elements of the iteration; completing the dimension expansion process by relevant spanning elements of the iteration and reducing a power consumption of irrelevant spanning elements until, at least, a completion of the applying of the dimension expansion process; determining identifiers that may be associated with significant portions of an output of the multiple iterations; and providing a signature that may include the identifiers and represents the multiple objects.

The identifiers may be retrieval information for retrieving the significant portions.

The at least some of the multiple iterations may be a majority of the multiple iterations.

The output of the multiple iteration may include multiple property attributes for each segment out of multiple segments of the media unit; and wherein the significant portions of an output of the multiple iterations may include more impactful property attributes.

A first iteration of the multiple iteration may include applying the dimension expansion process by applying different filters on the media unit.

The at least some of the multiple iteration exclude at least a first iteration of the multiple iterations.

The determining the relevancy of the spanning elements of the iteration may be based on at least some identities of relevant spanning elements of at least one previous iteration.

The determining the relevancy of the spanning elements of the iteration may be based on at least some identities of relevant spanning elements of at least one previous iteration that preceded the iteration.

The determining the relevancy of the spanning elements of the iteration may be based on properties of the media unit.

The determining the relevancy of the spanning elements of the iteration may be performed by the spanning elements of the iteration.

The method may include a neural network processing operation that may be executed by one or more layers of a neural network and does not belong to the at least some of the multiple iterations.

The at least one iteration may be executed without reducing power consumption of irrelevant neurons of the one or more layers.

The one or more layers output information about properties of the media unit, wherein the information differs from a recognition of the multiple objects.

The applying, by spanning elements of an iteration that differs from the first iteration, the dimension expansion process may include assigning output values that may be indicative of an identity of the relevant spanning elements of the iteration.

The applying, by spanning elements of an iteration that differs from the first iteration, the dimension expansion process may include assigning output values that may be indicative a history of dimension expansion processes until the iteration that differs from the first iteration.

Each spanning element may be associated with a subset of reference identifiers; and wherein the determining of the relevancy of each spanning elements of the iteration may be based a relationship between the subset of the reference identifiers of the spanning element and an output of a last merge operation before the iteration.

An output of a dimension expansion process of an iteration may be a multidimensional representation of the media unit that may include media unit regions of interest that may be associated with one or more expansion processes that generated the regions of interest.

A merge operation of the iteration may include selecting a subgroup of media unit regions of interest based on a spatial relationship between the subgroup of multidimensional regions of interest.

The method may include applying a merge function on the subgroup of multidimensional regions of interest.

The method may include applying an intersection function on the subgroup of multidimensional regions of interest.

A merge operation of the iteration may be based on an actual size of one or more multidimensional regions of interest.

A merge operation of the iteration may be based on relationship between sizes of the multidimensional regions of interest.

A merge operation of the iteration may be based on changes of the media unit regions of interest during at least the iteration and one or more previous iteration.

There may be provided a non-transitory computer readable medium for low-power calculation of a signature, the non-transitory computer readable medium may store instructions for receiving or generating a media unit of multiple objects; processing the media unit by performing multiple iterations, wherein at least some of the multiple iterations may include applying, by spanning elements of the iteration, dimension expansion process that may be followed by a merge operation; wherein the applying of the dimension expansion process of an iteration may include determining a relevancy of the spanning elements of the iteration; completing the dimension expansion process by relevant spanning elements of the iteration and reducing a power consumption of irrelevant spanning elements until, at least, a completion of the applying of the dimension expansion process; determining identifiers that may be associated with significant portions of an output of the multiple iterations; and providing a signature that may include the identifiers and represents the multiple objects.

The identifiers may be retrieval information for retrieving the significant portions.

The at least some of the multiple iterations may be a majority of the multiple iterations.

The output of the multiple iteration may include multiple property attributes for each segment out of multiple segments of the media unit; and wherein the significant portions of an output of the multiple iterations may include more impactful property attributes.

A first iteration of the multiple iteration may include applying the dimension expansion process by applying different filters on the media unit.

The at least some of the multiple iteration exclude at least a first iteration of the multiple iterations.

The determining the relevancy of the spanning elements of the iteration may be based on at least some identities of relevant spanning elements of at least one previous iteration.

The determining the relevancy of the spanning elements of the iteration may be based on at least some identities of relevant spanning elements of at least one previous iteration that preceded the iteration.

The determining the relevancy of the spanning elements of the iteration may be based on properties of the media unit.

The determining the relevancy of the spanning elements of the iteration may be performed by the spanning elements of the iteration.

The non-transitory computer readable medium may store instructions for performing a neural network processing operation, by one or more layers of a neural network, wherein the neural network processing operation and does not belong to the at least some of the multiple iterations.

The non-transitory computer readable medium the at least one iteration may be executed without reducing power consumption of irrelevant neurons of the one or more layers.

The one or more layers output information about properties of the media unit, wherein the information differs from a recognition of the multiple objects.

The applying, by spanning elements of an iteration that differs from the first iteration, the dimension expansion process may include assigning output values that may be indicative of an identity of the relevant spanning elements of the iteration.

The applying, by spanning elements of an iteration that differs from the first iteration, the dimension expansion process may include assigning output values that may be indicative a history of dimension expansion processes until the iteration that differs from the first iteration.

Each spanning element may be associated with a subset of reference identifiers; and wherein the determining of the relevancy of each spanning elements of the iteration may be based a relationship between the subset of the reference identifiers of the spanning element and an output of a last merge operation before the iteration.

An output of a dimension expansion process of an iteration may be a multidimensional representation of the media unit that may include media unit regions of interest that may be associated with one or more expansion processes that generated the regions of interest.

A merge operation of the iteration may include selecting a subgroup of media unit regions of interest based on a spatial relationship between the subgroup of multidimensional regions of interest.

The non-transitory computer readable medium may store instructions for applying a merge function on the subgroup of multidimensional regions of interest.

The non-transitory computer readable medium may store instructions for applying an intersection function on the subgroup of multidimensional regions of interest.

A merge operation of the iteration may be based on an actual size of one or more multidimensional regions of interest.

A merge operation of the iteration may be based on relationship between sizes of the multidimensional regions of interest.

A merge operation of the iteration may be based on changes of the media unit regions of interest during at least the iteration and one or more previous iteration.

There may be provided a signature generator that may include an input that may be configured to receive or generate a media unit of multiple objects; an processor that may be configured to process the media unit by performing multiple iterations, wherein at least some of the multiple iterations may include applying, by spanning elements of the iteration, dimension expansion process that may be followed by a merge operation; wherein the applying of the dimension expansion process of an iteration may include determining a relevancy of the spanning elements of the iteration; completing the dimension expansion process by relevant spanning elements of the iteration and reducing a power consumption of irrelevant spanning elements until, at least, a completion of the applying of the dimension expansion process; an identifier unit that may be configured to determine identifiers that may be associated with significant portions of an output of the multiple iterations; and an output that may be configured to provide a signature that may include the identifiers and represents the multiple objects.

The identifiers may be retrieval information for retrieving the significant portions.

The at least some of the multiple iterations may be a majority of the multiple iterations.

The output of the multiple iteration may include multiple property attributes for each segment out of multiple segments of the media unit; and wherein the significant portions of an output of the multiple iterations may include more impactful property attributes.

A first iteration of the multiple iteration may include applying the dimension expansion process by applying different filters on the media unit.

The at least some of the multiple iteration exclude at least a first iteration of the multiple iterations.

The determining the relevancy of the spanning elements of the iteration may be based on at least some identities of relevant spanning elements of at least one previous iteration.

The determining the relevancy of the spanning elements of the iteration may be based on at least some identities of relevant spanning elements of at least one previous iteration that preceded the iteration.

The determining the relevancy of the spanning elements of the iteration may be based on properties of the media unit.

The determining the relevancy of the spanning elements of the iteration may be performed by the spanning elements of the iteration.

The signature generator that may include one or more layers of a neural network that may be configured to perform a neural network processing operation, wherein the neural network processing operation does not belong to the at least some of the multiple iterations.

At least one iteration may be executed without reducing power consumption of irrelevant neurons of the one or more layers.

The one or more layers output information about properties of the media unit, wherein the information differs from a recognition of the multiple objects.

The applying, by spanning elements of an iteration that differs from the first iteration, the dimension expansion process may include assigning output values that may be indicative of an identity of the relevant spanning elements of the iteration.

The applying, by spanning elements of an iteration that differs from the first iteration, the dimension expansion process may include assigning output values that may be indicative a history of dimension expansion processes until the iteration that differs from the first iteration.

Each spanning element may be associated with a subset of reference identifiers; and wherein the determining of the relevancy of each spanning elements of the iteration may be based a relationship between the subset of the reference identifiers of the spanning element and an output of a last merge operation before the iteration.

An output of a dimension expansion process of an iteration may be a multidimensional representation of the media unit that may include media unit regions of interest that may be associated with one or more expansion processes that generated the regions of interest.

A merge operation of the iteration may include selecting a subgroup of media unit regions of interest based on a spatial relationship between the subgroup of multidimensional regions of interest.

The signature generator that may be configured to apply a merge function on the subgroup of multidimensional regions of interest.

The signature generator that may be configured to apply an intersection function on the subgroup of multidimensional regions of interest.

A merge operation of the iteration may be based on an actual size of one or more multidimensional regions of interest.

A merge operation of the iteration may be based on relationship between sizes of the multidimensional regions of interest.

A merge operation of the iteration may be based on changes of the media unit regions of interest during at least the iteration and one or more previous iteration.

There may be provided There may be provided a method for low-power calculation of a signature of a media unit by a group of calculating elements, the method may include calculating, multiple attributes of segments of the media unit; wherein the calculating may include determining, by each calculating element of multiple calculating elements, a relevancy of the calculation unit to the media unit to provide irrelevant calculating elements and relevant calculating elements; reducing a power consumption of each irrelevant calculating element; and completing the calculating of the multiple attributes of segments of the media unit by the relevant calculating elements; determining identifiers that may be associated with significant attributes out of the multiple attributes of segments of the media unit; and providing a signature that may include the identifiers and represents the multiple objects.

The calculating elements may be spanning elements.

Each calculating element may be associated with a subset of one or more reference identifiers; and wherein the determining of a relevancy of the calculation unit to the media unit may be based on a relationship between the subset and the identifiers related to the media unit.

Each calculating element may be associated with a subset of one or more reference identifiers; and wherein a calculation element may be relevant to the media unit when the identifiers related to the media unit may include each reference identifier of the subset.

The calculating of the multiple attributes of segments of the media unit may be executed in multiple iterations; and wherein each iteration may be executed by calculation elements associated with the iteration; wherein the determining, by each calculating element of multiple calculating elements, of the relevancy of the calculation unit to the media unit may be executed per iteration.

The multiple iterations may be preceded by a calculation of initial media unit attributes by one or more layers of a neural network.

There may be provided a non-transitory computer readable medium for low-power calculation of a signature of a media unit by a group of calculating elements, the non-transitory computer readable medium may store instructions for calculating, multiple attributes of segments of the media unit; wherein the calculating may include determining, by each calculating element of multiple calculating elements, a relevancy of the calculation unit to the media unit to provide irrelevant calculating elements and relevant calculating elements; reducing a power consumption of each irrelevant calculating element; and completing the calculating of the multiple attributes of segments of the media unit by the relevant calculating elements; determining identifiers that may be associated with significant attributes out of the multiple attributes of segments of the media unit; and providing a signature that may include the identifiers and represents the multiple objects.

The calculating elements may be spanning elements.

Each calculating element may be associated with a subset of one or more reference identifiers; and wherein the determining of a relevancy of the calculation unit to the media unit may be based on a relationship between the subset and the identifiers related to the media unit.

Each calculating element may be associated with a subset of one or more reference identifiers; and wherein a calculation element may be relevant to the media unit when the identifiers related to the media unit may include each reference identifier of the subset.

The calculating of the multiple attributes of segments of the media unit may be executed in multiple iterations; and wherein each iteration may be executed by calculation elements associated with the iteration; wherein the determining, by each calculating element of multiple calculating elements, of the relevancy of the calculation unit to the media unit may be executed per iteration.

The multiple iterations may be preceded by a calculation of initial media unit attributes by one or more layers of a neural network.

There may be provided a signature generator that may include a processor that may be configured to calculate multiple attributes of segments of the media unit; wherein the calculating may include determining, by each calculating element of multiple calculating elements of the processor, a relevancy of the calculation unit to the media unit to provide irrelevant calculating elements and relevant calculating elements; reducing a power consumption of each irrelevant calculating element; and completing the calculating of the multiple attributes of segments of the media unit by the relevant calculating elements; an identifier unit that may be configured to determine identifiers that may be associated with significant attributes out of the multiple attributes of segments of the media unit; and an output that may be configured to provide a signature that may include the identifiers and represents the multiple objects.

The calculating elements may be spanning elements.

Each calculating element may be associated with a subset of one or more reference identifiers; and wherein the determining of a relevancy of the calculation unit to the media unit may be based on a relationship between the subset and the identifiers related to the media unit.

Each calculating element may be associated with a subset of one or more reference identifiers; and wherein a calculation element may be relevant to the media unit when the identifiers related to the media unit may include each reference identifier of the subset.

The calculating of the multiple attributes of segments of the media unit may be executed in multiple iterations; and wherein each iteration may be executed by calculation elements associated with the iteration; wherein the determining, by each calculating element of multiple calculating elements, of the relevancy of the calculation unit to the media unit may be executed per iteration.

The multiple iterations may be preceded by a calculation of initial media unit attributes by one or more layers of a neural network.

There may be provided a method for generating a hybrid representation of a media unit, the method may include receiving or generating the media unit; processing the media unit by performing multiple iterations, wherein at least some of the multiple iterations may include applying, by spanning elements of the iteration, dimension expansion process that may be followed by a merge operation; selecting, based on an output of the multiple iterations, media unit regions of interest that contributed to the output of the multiple iterations; and providing the hybrid representation, wherein the hybrid representation may include shape information regarding shapes of the media unit regions of interest, and a media unit signature that may include identifiers that identify the media unit regions of interest.

The selecting of the media regions of interest may be executed per segment out of multiple segments of the media unit.

The shape information may include polygons that represent shapes that substantially bound the media unit regions of interest.

The providing of the hybrid representation of the media unit may include compressing the shape information of the media unit to provide compressed shape information of the media unit.

The method may include comparing the media unit signature of the media unit to signatures of multiple concept structures to find a matching concept structure that has at least one matching signature that matches to the media unit signature; and calculating higher accuracy shape information that may be related to regions of interest of the media unit, wherein the higher accuracy shape information may be of higher accuracy than the compressed shape information of the media unit, wherein the calculating may be based on shape information associated with at least some of the matching signatures.

The method may include determining shapes of the media unit regions of interest using the higher accuracy shape information.

For each media unit region of interest, the calculating of the higher accuracy shape information may include virtually overlaying shapes of corresponding media units of interest of at least some of the matching signatures.

There may be provided a non-transitory computer readable medium for generating a hybrid representation of a media unit, the non-transitory computer readable medium may store instructions for receiving or generating the media unit; processing the media unit by performing multiple iterations, wherein at least some of the multiple iterations may include applying, by spanning elements of the iteration, dimension expansion process that may be followed by a merge operation; selecting, based on an output of the multiple iterations, media unit regions of interest that contributed to the output of the multiple iterations; and providing the hybrid representation, wherein the hybrid representation may include shape information regarding shapes of the media unit regions of interest, and a media unit signature that may include identifiers that identify the media unit regions of interest.

The selecting of the media regions of interest may be executed per segment out of multiple segments of the media unit.

The shape information may include polygons that represent shapes that substantially bound the media unit regions of interest.

The providing of the hybrid representation of the media unit may include compressing the shape information of the media unit to provide compressed shape information of the media unit.

The non-transitory computer readable medium may store instructions for comparing the media unit signature of the media unit to signatures of multiple concept structures to find a matching concept structure that has at least one matching signature that matches to the media unit signature; and calculating higher accuracy shape information that may be related to regions of interest of the media unit, wherein the higher accuracy shape information may be of higher accuracy than the compressed shape information of the media unit, wherein the calculating may be based on shape information associated with at least some of the matching signatures.

The non-transitory computer readable medium may store instructions for determining shapes of the media unit regions of interest using the higher accuracy shape information.

The for each media unit region of interest, the calculating of the higher accuracy shape information may include virtually overlaying shapes of corresponding media units of interest of at least some of the matching signatures.

There may be provided a hybrid representation generator for generating a hybrid representation of a media unit, the hybrid representation generator may include an input that may be configured to receive or generate the media unit; a processor that may be configured to process the media unit by performing multiple iterations, wherein at least some of the multiple iterations may include applying, by spanning elements of the iteration, dimension expansion process that may be followed by a merge operation; a selection unit that may be configured to select, based on an output of the multiple iterations, media unit regions of interest that contributed to the output of the multiple iterations; and an output that may be configured to provide the hybrid representation, wherein the hybrid representation may include shape information regarding shapes of the media unit regions of interest, and a media unit signature that may include identifiers that identify the media unit regions of interest.

The selecting of the media regions of interest may be executed per segment out of multiple segments of the media unit.

The shape information may include polygons that represent shapes that substantially bound the media unit regions of interest.

The hybrid representation generator that may be configured to compress the shape information of the media unit to provide compressed shape information of the media unit.

There may be provided a method for scale invariant object detection, the method may include receiving or generating a first image in which an object appears in a first scale and a second image in which the object appears in a second scale that differs from the first scale; generating a first image signature and a second image signature; wherein the first image signature may include a first group of at least one certain first image identifier that identifies at least a part of the object; wherein the second image signature may include a second group of certain second image identifiers that identify different parts of the object; wherein the second group may be larger than first group; and linking between the at least one certain first image identifier and the certain second image identifiers.

The method may include linking between the first image signature, the second image signature and the object.

The linking may include adding the first signature and the second signature to a certain concept structure that may be associated with the object.

The method may include receiving or generating an input image; generating a signature of the input image; comparing the signature of the input image to signatures of the certain concept structure; and determining that the input image may include the object when at least one of the signatures of the certain concept structure matches the signature of the input image.

The method may include receiving or generating an input image; generating a signature of the input image, the signature of the input image may include only some of the certain second image identifiers; wherein the input image of the second scale; changing a scale of the input image to the first scale to a provide an amended input image; generating a signature of the amended input image; and verifying that the input image may include the object when the signature of the amended input image may include the at least one certain first image identifier.

The method may include receiving or generating an input image; generating a signature of the input image; searching in the signature of the input image for at least one of (a) the at least one certain first image identifier, and (b) the certain second image identifiers; and determining that the input image may include the object when the signature of the input image may include the at least one of (a) the at least one certain first image identifier, and (b) the certain second image identifiers.

The method may include receiving or generating an input image; generating a signature of the input image; searching in the signature of the input image for at least one of (a) one or more certain first image identifier of the at least one certain first image identifier, and (b) at least one certain second image identifier of the certain second image identifiers; and determining that a input image includes the object when the signature of the input image may include the at least one of (a) one or more certain first image identifier of the at least one certain first image identifier, and (b) the at least one certain second image identifier.

There may be provided a non-transitory computer readable medium for scale invariant object detection, the non-transitory computer readable medium may store instructions for receiving or generating a first image in which an object appears in a first scale and a second image in which the object appears in a second scale that differs from the first scale; generating a first image signature and a second image signature; wherein the first image signature may include a first group of at least one certain first image identifier that identifies at least a part of the object; wherein the second image signature may include a second group of certain second image identifiers that identify different parts of the object; wherein the second group may be larger than first group; and linking between the at least one certain first image identifier and the certain second image identifiers.

The non-transitory computer readable medium may store instructions for linking between the first image signature, the second image signature and the object.

The linking may include adding the first signature and the second signature to a certain concept structure that may be associated with the object.

The non-transitory computer readable medium may store instructions for receiving or generating an input image; generating a signature of the input image; comparing the signature of the input image to signatures of the certain concept structure; and determining that the input image may include the object when at least one of the signatures of the certain concept structure matches the signature of the input image.

The non-transitory computer readable medium may store instructions for receiving or generating an input image; generating a signature of the input image, the signature of the input image may include only some of the certain second image identifiers; wherein the input image of the second scale; changing a scale of the input image to the first scale to a provide an amended input image; generating a signature of the amended input image; and verifying that the input image may include the object when the signature of the amended input image may include the at least one certain first image identifier.

The non-transitory computer readable medium may store instructions for receiving or generating an input image; generating a signature of the input image; searching in the signature of the input image for at least one of (a) the at least one certain first image identifier, and (b) the certain second image identifiers; and determining that a input image includes the object when the signature of the input image may include the at least one of (a) the at least one certain first image identifier, and (b) the certain second image identifiers.

The non-transitory computer readable medium may store instructions for receiving or generating an input image; generating a signature of the input image; searching in the signature of the input image for at least one of (a) one or more certain first image identifier of the at least one certain first image identifier, and (b) at least one certain second image identifier of the certain second image identifiers; and determining that a input image includes the object when the signature of the input image may include the at least one of (a) one or more certain first image identifier of the at least one certain first image identifier, and (b) the at least one certain second image identifier.

There may be provided an object detector for scale invariant object detection, that may include an input that may be configured to receive a first image in which an object appears in a first scale and a second image in which the object appears in a second scale that differs from the first scale; a signature generator that may be configured to generate a first image signature and a second image signature; wherein the first image signature may include a first group of at least one certain first image identifier that identifies at least a part of the object; wherein the second image signature may include a second group of certain second image identifiers that identify different parts of the object; wherein the second group may be larger than first group; and an object detection determination unit that may be configured to link between the at least one certain first image identifier and the certain second image identifiers.

The object detection determination unit may be configured to link between the first image signature, the second image signature and the object.

The object detection determination unit may be configured to add the first signature and the second signature to a certain concept structure that may be associated with the object.

The input may be configured to receive an input image; wherein the signal generator may be configured to generate a signature of the input image; wherein the object detection determination unit may be configured to compare the signature of the input image to signatures of the certain concept structure, and to determine that the input image may include the object when at least one of the signatures of the certain concept structure matches the signature of the input image.

The input may be configured to receive an input image; wherein the signature generator may be configured to generate a signature of the input image, the signature of the input image may include only some of the certain second image identifiers; wherein the input image of the second scale; wherein the input may be configured to receive an amended input image that was generated by changing a scale of the input image to the first scale; wherein the signature generator may be configured to generate a signature of the amended input image; and wherein the object detection determination unit may be configured to verify that the input image may include the object when the signature of the amended input image may include the at least one certain first image identifier.

The input may be configured to receive an input image; wherein the signature generator may be configured to generate a signature of the input image; wherein the object detection determination unit may be configured to search in the signature of the input image for at least one of (a) the at least one certain first image identifier, and (b) the certain second image identifiers; and determine that a input image includes the object when the signature of the input image may include the at least one of (a) the at least one certain first image identifier, and (b) the certain second image identifiers.

The input may be configured to receive an input image; wherein the signature generator may be configured to generate a signature of the input image; wherein the object detection determination unit may be configured to search in the signature of the input image for at least one of (a) one or more certain first image identifier of the at least one certain first image identifier, and (b) at least one certain second image identifier of the certain second image identifiers; and determine that a input image includes the object when the signature of the input image may include the at least one of (a) one or more certain first image identifier of the at least one certain first image identifier, and (b) the at least one certain second image identifier.

There may be provided a method for movement based object detection, the method may include receiving or generating a video stream that may include a sequence of images; generating image signatures of the images; wherein each image may be associated with an image signature that may include identifiers; wherein each identifier identifiers a region of interest within the image; generating movement information indicative of movements of the regions of interest within consecutive images of the sequence of images; searching, based on the movement information, for a first group of regions of interest that follow a first movement; wherein different first regions of interest may be associated with different parts of an object; and linking between first identifiers that identify the first group of regions of interest.

The linking may include linking between first image signatures that include the first linked identifiers.

The linking may include adding the first image signatures to a first concept structure, the first concept structure may be associated with the first image.

The method may include receiving or generating an input image; generating a signature of the input image; comparing the signature of the input image to signatures of the first concept structure; and determining that the input image may include a first object when at least one of the signatures of the first concept structure matches the signature of the input image.

The method may include receiving or generating an input image; generating a signature of the input image; searching in the signature of the input image for at least one of the first identifiers; and determining that the input image may include the object when the signature of the input image may include at least one of the first identifiers.

The method may include generating location information indicative of a location of each region of interest within each image; wherein the generating movement information may be based on the location information.

There may be provided a non-transitory computer readable medium for movement based object detection, the non-transitory computer readable medium may include receiving or generating a video stream that may include a sequence of images; generating image signatures of the images; wherein each image may be associated with an image signature that may include identifiers; wherein each identifier identifiers a region of interest within the image; wherein different region of interests include different objects; generating movement information indicative of movements of the regions of interest within consecutive images of the sequence of images; searching, based on the movement information, for a first group of regions of interest that follow a first movement; and linking between first identifiers that identify the first group of regions of interest.

The linking may include linking between first image signatures that include the first linked identifiers.

The linking may include adding the first image signatures to a first concept structure, the first concept structure may be associated with the first image.

The non-transitory computer readable medium may store instructions for receiving or generating an input image; generating a signature of the input image; comparing the signature of the input image to signatures of the first concept structure; and determining that the input image may include a first object when at least one of the signatures of the first concept structure matches the signature of the input image.

The non-transitory computer readable medium may store instructions for receiving or generating an input image; generating a signature of the input image; searching in the signature of the input image for at least one of the first identifiers; and determining that the input image may include the object when the signature of the input image may include at least one of the first identifiers.

The non-transitory computer readable medium may store instructions for generating location information indicative of a location of each region of interest within each image; wherein the generating movement information may be based on the location information.

There may be provided an object detector that may include an input that may be configured to receive a video stream that may include a sequence of images; a signature generator that may be configured to generate image signatures of the images; wherein each image may be associated with an image signature that may include identifiers; wherein each identifier identifiers a region of interest within the image; a movement information unit that may be is configured to generate movement information indicative of movements of the regions of interest within consecutive images of the sequence of images; an object detection determination unit that may be configured to search, based on the movement information, for a first group of regions of interest that follow a first movement; wherein different first regions of interest may be associated with different parts of an object; and link between first identifiers that identify the first group of regions of interest.

The linking may include linking between first image signatures that include the first linked identifiers.

The linking may include adding the first image signatures to a first concept structure, the first concept structure may be associated with the first image.

The input may be configured to receive an input image; wherein the signature generator may be configured to generate a signature of the input image; and wherein the object detection determination unit may be configured to compare the signature of the input image to signatures of the first concept structure; and to determine that the input image may include a first object when at least one of the signatures of the first concept structure matches the signature of the input image.

The input may be configured to receive an input image; wherein the signature generator may be configured to generate a signature of the input image; and wherein the object detection determination unit may be configured to search in the signature of the input image for at least one of the first identifiers; and to determine that the input image may include the object when the signature of the input image may include at least one of the first identifiers.

The object detector that may be configured to generate location information indicative of a location of each region of interest within each image; wherein the generating of the movement information may be based on the location information.

There may be provided a method for object detection, the method may include receiving an input image; generating a signature of the input image; comparing the signature of the input image to signatures of a concept structure; determining whether the signature of the input image matches any of the signatures of the concept structure based on signature matching criteria, wherein each signature of the concept structure may be associated within a signature matching criterion that may be determined based on an object detection parameter of the signature; and concluding that the input image may include an object associated with the concept structure based on an outcome of the determining.

Each signature matching criterion may be determined by evaluating object detection capabilities of the signature under different signature matching criteria.

The evaluating of the object detection capabilities of the signature under different signature matching criteria may include receiving or generating signatures of a group of test images; calculating the object detection capability of the signature, for each signature matching criterion of the different signature matching criteria; and selecting the signature matching criterion based on the object detection capabilities of the signature under the different signature matching criteria.

The object detection capability reflects a percent of signatures of the group of test images that match the signature.

The selecting of the signature matching criterion may include selecting the signature matching criterion that one applied results in a percent of signatures of the group of test images that match the signature that may be closets to a predefined desired percent of signatures of the group of test images that match the signature.

The signature matching criteria may be a minimal number of matching identifiers that indicate of a match.

There may be provided a method for managing a concept structure, the method may include determining to add a new signature to the concept structure, wherein the concept structure already may include at least one old signature; wherein the new signature may include identifiers that identify at least parts of objects; and determining a new signature matching criterion that may be based on one or more of the identifiers of the new signature; wherein the new signature matching criterion determines when another signature matches the new signature; wherein the determining of the new signature matching criterion may include evaluating object detection capabilities of the signature under different signature matching criteria.

The evaluating of the object detection capabilities of the signature under different signature matching criteria may include receiving or generating signatures of a group of test images; calculating the object detection capability of the signature, for each signature matching criterion of the different signature matching criteria; and selecting the signature matching criterion based on the object detection capabilities of the signature under the different signature matching criteria.

The object detection capability reflects a percent of signatures of the group of test images that match the signature.

The selecting of the signature matching criterion may include selecting the signature matching criterion that one applied results in a percent of signatures of the group of test images that match the signature that may be closets to a predefined desired percent of signatures of the group of test images that match the signature.

The signature matching criteria may be a minimal number of matching identifiers that indicate of a match.

There may be provided a non-transitory computer readable medium for object detection, the non-transitory computer readable medium may store instructions for receiving an input image; generating a signature of the input image; comparing the signature of the input image to signatures of a concept structure; determining whether the signature of the input image matches any of the signatures of the concept structure based on signature matching criteria, wherein each signature of the concept structure may be associated within a signature matching criterion that may be determined based on an object detection parameter of the signature; and concluding that the input image may include an object associated with the concept structure based on an outcome of the determining.

Each signature matching criterion may be determined by evaluating object detection capabilities of the signature under different signature matching criteria.

The evaluating of the object detection capabilities of the signature under different signature matching criteria may include receiving or generating signatures of a group of test images; calculating the object detection capability of the signature, for each signature matching criterion of the different signature matching criteria; and selecting the signature matching criterion based on the object detection capabilities of the signature under the different signature matching criteria.

The object detection capability reflects a percent of signatures of the group of test images that match the signature.

The selecting of the signature matching criterion may include selecting the signature matching criterion that one applied results in a percent of signatures of the group of test images that match the signature that may be closets to a predefined desired percent of signatures of the group of test images that match the signature.

The signature matching criteria may be a minimal number of matching identifiers that indicate of a match.

There may be provided a non-transitory computer readable medium for managing a concept structure, the non-transitory computer readable medium may store instructions for determining to add a new signature to the concept structure, wherein the concept structure already may include at least one old signature; wherein the new signature may include identifiers that identify at least parts of objects; and determining a new signature matching criterion that may be based on one or more of the identifiers of the new signature; wherein the new signature matching criterion determines when another signature matches the new signature; wherein the determining of the new signature matching criterion may include evaluating object detection capabilities of the signature under different signature matching criteria.

The evaluating of the object detection capabilities of the signature under different signature matching criteria may include receiving or generating signatures of a group of test images; calculating the object detection capability of the signature, for each signature matching criterion of the different signature matching criteria; and selecting the signature matching criterion based on the object detection capabilities of the signature under the different signature matching criteria.

The object detection capability reflects a percent of signatures of the group of test images that match the signature.

The selecting of the signature matching criterion may include selecting the signature matching criterion that one applied results in a percent of signatures of the group of test images that match the signature that may be closets to a predefined desired percent of signatures of the group of test images that match the signature.

The signature matching criteria may be a minimal number of matching identifiers that indicate of a match.

There may be provided an object detector that may include an input that may be configured to receive an input image; a signature generator that may be configured to generate a signature of the input image; an object detection determination unit that may be configured to compare the signature of the input image to signatures of a concept structure; determine whether the signature of the input image matches any of the signatures of the concept structure based on signature matching criteria, wherein each signature of the concept structure may be associated within a signature matching criterion that may be determined based on an object detection parameter of the signature; and conclude that the input image may include an object associated with the concept structure based on an outcome of the determining.

The object detector according to claim, may include a signature matching criterion unit that may be configured to determine each signature matching criterion by evaluating object detection capabilities of the signature under different signature matching criteria.

The input may be configured to receive signatures of a group of test images; wherein the signature matching criterion unit may be configured to calculate the object detection capability of the signature, for each signature matching criterion of the different signature matching criteria; and select the signature matching criterion based on the object detection capabilities of the signature under the different signature matching criteria.

The object detector according to claim wherein the object detection capability reflects a percent of signatures of the group of test images that match the signature.

The object detector according to claim wherein the signature matching criterion unit may be configured to select the signature matching criterion that one applied results in a percent of signatures of the group of test images that match the signature that may be closets to a predefined desired percent of signatures of the group of test images that match the signature.

The object detector according to claim wherein the signature matching criteria may be a minimal number of matching identifiers that indicate of a match.

There may be provided a concept structure manager that may include a controller that may be configured to determine to add a new signature to the concept structure, wherein the concept structure already may include at least one old signature; wherein the new signature may include identifiers that identify at least parts of objects; and a signature matching criterion unit that may be configured to determine a new signature matching criterion that may be based on one or more of the identifiers of the new signature; wherein the new signature matching criterion determines when another signature matches the new signature; wherein the determining of the new signature matching criterion may include evaluating object detection capabilities of the signature under different signature matching criteria.

The signature matching criterion unit may be configured to determine each signature matching criterion by evaluating object detection capabilities of the signature under different signature matching criteria.

The input may be configured to receive signatures of a group of test images; wherein the signature matching criterion unit may be configured to calculate the object detection capability of the signature, for each signature matching criterion of the different signature matching criteria; and select the signature matching criterion based on the object detection capabilities of the signature under the different signature matching criteria.

The concept manager according to claim wherein the object detection capability reflects a percent of signatures of the group of test images that match the signature.

The concept manager according to claim wherein the signature matching criterion unit may be configured to select the signature matching criterion that one applied results in a percent of signatures of the group of test images that match the signature that may be closets to a predefined desired percent of signatures of the group of test images that match the signature.

Obstacle Avoidance

It will be appreciated that in “normal”, non-assisted driving scenarios, a driver may encounter driving obstacles that are not necessarily indicated in commonly available roadmaps. For example, it is not uncommon for a roadway to have potholes that may complicate the driving process. Some potholes are large enough to cause damage to the wheels or undercarriage of the vehicle. Accordingly, when encountering a pothole, the driver may swerve to avoid it. However, depending on the layout of the roadway and/or traffic conditions, it may not be feasible for the driver to react in such a manner. It is also possible that depending on current visibility, the speed of the vehicle, and/or the alertness of the driver, the driver may not even see the pothole in time to avoid it.

It will be appreciated that such issues are also relevant in an assisted/autonomous driving scenario. As discussed hereinabove, such systems may use one or more sensors to acquire information about the current driving environment in order to determine how to drive along the roadway as per a stored map. It is possible, that the vehicle's sensor(s) may not detect an obstacle, e.g., a pothole, sufficiently in advance in order to enable the system to determine that evasive action should be taken to avoid the obstacle and to perform that action in time. It will be appreciated that such a scenario may be further complicated by the presence of other vehicles and/or pedestrians in the immediate vicinity of the vehicle.

Reference is now made to FIG. 3A, which is a partly-pictorial, partly-block diagram illustration of an exemplary obstacle detection and mapping system 10 (hereinafter referred to also as “system 10”) constructed and operative in accordance with embodiments described herein. As described herein, system 10 may be operative to process sensor data to detect and map obstacles on a roadway in order to enable an assisted/autonomous driving system to predict and avoid obstacles in the path of a vehicle.

System 10 comprises vehicle 100 and obstacle avoidance server 400 which may be configured to communicate with each other over a communications network such as, for example, the Internet. In accordance with the exemplary embodiment of FIG. 3A, vehicle 100 may be configured with an autonomous driving system (not shown) operative to autonomously provide driving instructions to vehicle 100 without the intervention of a human driver. It will be appreciated that the embodiments described herein may also support the configuration of vehicle 100 with an assisted (or “semi-autonomous”) driving system where in at least some situations a human driver may take control of vehicle 100 and/or where in at least some situations the semi-autonomous driving system provides warnings to the driver without necessarily directly controlling vehicle 100.

In accordance with the exemplary embodiment of FIG. 3A, vehicle 100 may be configured with at least one sensor 130 to provide information about a current driving environment as vehicle 100 proceeds along roadway 20. It will be appreciated that while sensor 130 is depicted in FIG. 3A as a single entity, in practice, as will be described hereinbelow, there may be multiple sensors 130 arrayed on, or inside of, vehicle 130. In accordance with embodiments described herein, sensor(s) 130 may be implemented using a conventional camera operative to capture images of roadway 20 and objects in its immediate vicinity. It will be appreciated that sensor 130 may be implemented using any suitable imaging technology instead of, or in addition to, a conventional camera. For example, sensor 130 may also be operative to use infrared, radar imagery, ultrasound, electro-optics, radiography, LIDAR (light detection and ranging), etc. Furthermore, in accordance with some embodiments, one or more sensors 130 may also be installed independently along roadway 20, where information from such sensors 130 may be provided to vehicle 100 and/or obstacle avoidance server 400 as a service.

In accordance with the exemplary embodiment of FIG. 3A, static reference points 30A and 30B (collectively referred to hereinafter as static reference points 30) may be located along roadway 20. For example, static reference point 30A is depicted as a speed limit sign, and static reference point 30B is depicted as an exit sign. In operation, sensor 130 may capture images of static reference points 30. The images may then be processed by the autonomous driving system in vehicle 100 to provide information about the current driving environment for vehicle 100, e.g., the speed limit or the location of an upcoming exit.

Obstacle 40, e.g., a pothole, may be located on roadway 20. In accordance with embodiments described herein, sensor 130 may also be operative to capture images of obstacle 40. The autonomous driving system may be operative to detect the presence of obstacle 40 in the images provided by sensor 130 and to determine an appropriate response, e.g., whether or not vehicle 100 should change speed and/or direction to avoid or minimize the impact with obstacle 40. For example, if the autonomous driving system determines that obstacle 40 is a piece of paper on roadway 20, no further action may be necessary. However, if obstacle 40 is a pothole, the autonomous driving system may instruct vehicle 100 to slow down and/or swerve to avoid obstacle 40.

It will be appreciated that there may not be sufficient processing time for the autonomous driving system to determine an appropriate response; depending on the speed of vehicle 100 and the distance at which sensor 130 captures an image of obstacle 40, vehicle 100 may drive over/through obstacle 40 (or collide with it) before obstacle 40 is detected and/or an appropriate response may be determined by the autonomous driving system. In accordance with embodiments described herein, in order to provide additional processing time for obstacle detection and response determination, vehicle 100 may exchange obstacle information with obstacle avoidance server 400. The autonomous driving system may be operative to upload the imagery received from sensor 130 and/or the determination of the nature of obstacle 40 to obstacle avoidance server 400, and obstacle avoidance server 400 may be operative to provide obstacle information received in such manner from other vehicles 100 in order to enable the autonomous driving system to anticipate and respond to obstacles 40 in advance of, or in parallel to, receiving the relevant imagery from sensor 130.

Depending on the configuration of system 10 and vehicle 100, obstacle avoidance server 400 and vehicle 100 may exchange obstacle information in real-time (or near real-time) and/or in a burst mode, where the obstacle information may be provided either periodically and/or when conditions are suitable for a data exchange between obstacle avoidance server 400 and vehicle 100. For example, vehicle 100 may be configured to perform regular uploads/downloads of obstacle information when the engine is turned on (or off, with a keep alive battery function to facilitate communication with obstacle avoidance server 400). Alternatively, or in addition, vehicle 100 may be configured to perform such uploads/downloads when stationary (e.g., parked, or waiting at a traffic light) and a sufficiently reliable wireless connection is detected. Alternatively, or in addition, the uploads/downloads may be triggered by location, where obstacle information may be downloaded from obstacle avoidance server 400 when vehicle 100 enters an area for which it does not have up-to-date obstacle information.

It will also be appreciated that in some situations, the autonomous driving system may not detect obstacle 40 at all; it is possible that vehicle 100 may pass by/over/through obstacle 40 without detecting it. The autonomous driving system may have finite resources available to analyze imagery from sensor 130 in a finite period of time in order to determine a currently relevant driving policy. It is understandable that if, for whatever reason (e.g., driving speed, visibility, etc.), the autonomous driving system does not detect obstacle 40 in real-time or near-real time there may be little benefit to be realized from continuing processing the imagery on vehicle 100. However, the detection of a “missed” obstacle 40 may be of benefit to another vehicle 100 on roadway 20.

Accordingly, in some configurations of system 10, vehicle 100 may also be configured to upload raw driving data from sensor 130 for processing by obstacle avoidance server 400 to detect obstacles 40. Obstacle information associated with obstacles 40 detected in such manner may then be provided by obstacle avoidance server 400 as described hereinabove.

It will also be appreciated that it may be possible to use non-image data to detect obstacles 40 from the raw driving data. For example, at least one sensor 130 may be implemented as an electronic control unit (ECU) controlling the shock absorbers of vehicle 100. Obstacle avoidance server 400 may be configured to determine that one or more shocks of a certain magnitude and/or pattern registered by the shock absorber ECU may be indicative of a pothole.

In accordance with some embodiments described herein, the sources for raw driving data to be used by obstacle avoidance server 400 to detect obstacles 40 may not be limited to just vehicles 100 with an autonomous driving system. Obstacle avoidance server 400 may also receive raw driving data from vehicles that are being driven manually and/or with the assistance of a semi-autonomous driving system. It will be appreciated that in for such vehicles, the raw driving data may include indications of driver recognition of obstacles 40. For example, when a driver detects obstacle 40, e.g., sees a pothole, the driver may slow down in approach of obstacle 40, swerve to avoid it, and/or even change lanes. Accordingly, the raw driving data processed by obstacle avoidance server 400 may also include raw data uploaded from vehicles without an autonomous driving system.

Obstacle avoidance server 400 may use supervised and/or unsupervised learning methods to detect obstacles 40 in the raw driving data; reference data sets for the detection of obstacles 40 and the determination of associated driving policies may be prepared based on unsupervised analysis of raw driving data and/or using manual labelling and extraction.

It will be appreciated that it may not be a trivial task to pinpoint a location for obstacle 40. While it may generally be assumed that vehicle 100 is configured with a location-based service, e.g., global positioning satellite (GPS) system, such systems may not necessarily be accurate enough to provide a sufficient resolution regarding the expected location for obstacle 40. For example, one possible driving policy for evasive action may entail avoiding obstacle 40 by swerving or changing lanes on roadway 20. If the location-based service is only accurate to within five feet, swerving within the lane may actually increase the likelihood of vehicle 100 hitting the pothole. Similarly, it may not be possible to determine in which lane obstacle 40 is located.

In accordance with embodiments described herein, static reference points 30 may be used in system 10 to more accurately determine a location for obstacle 40; the location of obstacle 40 may be defined as an offset from a fixed location for one of static reference points 30. For example, in the exemplary embodiment of FIG. 3A, the location of obstacle 40 may be defined as “x” feet past static reference point 30A, where the distance in feet may be calculated as a function of the frames per second of the camera used for sensor 130 and the speed of vehicle 100. Alternatively, or in addition, two sensors 130 may be employed to use triangulation to determine the distance between static reference point 30A and obstacle 40. Alternatively, or in addition, the distance may be calculated using both reference points 30A and 30B to triangulate.

It will be appreciated that as described hereinabove, obstacle 40 may be a “persistent obstacle” in that it may be reasonably expected to remain on roadway 20 for an extended period of time. For example, potholes typically remain (and even increase in size) for several months before being repaired. However, some obstacles may be more transient in nature. For example, a tree fallen on the road or debris from a traffic accident may typically be removed from roadway 20 within a few hours. Such transient obstacles may, at least in some cases, be easier to detect than persistent obstacles, e.g., a fallen tree is easier to identifying from an approaching vehicle than a pothole.

It will therefore be appreciated that the driving policies for transient obstacles may be different than for persistent obstacles. For example, in a case where obstacle 40 is a pothole that may be assumed to be persistent, the associated driving policy may entail the autonomous driving system “erring on the side of caution” by performing evasive action (e.g., swerving or changing lanes) even if the pothole isn't detected in the data provided by sensor 130 when vehicle 100 reaches the pothole's expected location. However, in a case where obstacle 40 is a tree on roadway 20, the associated driving policy may entail the autonomous driving system performing preventative action, e.g., slowing down, prior to reaching the tree's expected location, but not to perform evasive action if the tree isn't actually detected according to the data provided by sensor 130.

It will also be appreciated that some obstacles may be “recurring obstacles” where transient obstacles recur in the same location. For example, recurring obstacle 50 represents a slight depression in roadway 20. The impact on vehicle 100 as it drives over a slight depression in roadway 20 may be minimal. However, in some cases it may be observed that the slight depression may fill with water or mud when it rains, or intermittently ice over during the winter. Accordingly, the driving policies for recurring obstacles 50 may more heavily weight other environmental factors, e.g., season or weather, than the driving policies for obstacles 40.

Reference is now made to FIG. 3B which is a block diagram of an exemplary autonomous driving system 200 (hereinafter also referred to as system 200), constructed and implemented in accordance with embodiments described herein. Autonomous driving system 200 comprises processing circuitry 210, input/output (I/O) module 220, camera 230, telemetry ECU 240, shock sensor 250, autonomous driving manager 260, and obstacle database 270. Autonomous driving manager 260 may be instantiated in a suitable memory for storing software such as, for example, an optical storage medium, a magnetic storage medium, an electronic storage medium, and/or a combination thereof. It will be appreciated that autonomous driving system 200 may be implemented as an integrated component of an onboard computer system in a vehicle, such as, for example, vehicle 100 from FIG. 3A. Alternatively, system 200 may be implemented and a separate component in communication with the onboard computer system. It will also be appreciated that in the interests of clarity, while autonomous driving system 200 may comprise additional components and/or functionality e.g., for autonomous driving of vehicle 100, such additional components and/or functionality are not depicted in FIG. 3B and/or described herein.

Processing circuitry 210 may be operative to execute instructions stored in memory (not shown). For example, processing circuitry 210 may be operative to execute autonomous driving manager 260. It will be appreciated that processing circuitry 210 may be implemented as a central processing unit (CPU), and/or one or more other integrated circuits such as application-specific integrated circuits (ASICs), field programmable gate arrays (FPGAs), full-custom integrated circuits, etc., or a combination of such integrated circuits. It will similarly be appreciated that autonomous driving system 200 may comprise more than one instance of processing circuitry 210. For example, one such instance of processing circuitry 210 may be a special purpose processor operative to execute autonomous driving manager 260 to perform some, or all, of the functionality of autonomous driving system 200 as described herein.

I/O module 220 may be any suitable communications component such as a network interface card, universal serial bus (USB) port, disk reader, modem or transceiver that may be operative to use protocols such as are known in the art to communicate either directly, or indirectly, with other elements of system 10 (FIG. 3A) and/or system 200, such as, for example, obstacle avoidance server 400 (FIG. 3A), camera 230, telemetry ECU 240, and/or shock sensor 250. As such, I/O module 220 may be operative to use a wired or wireless connection to connect to obstacle avoidance server 400 via a communications network such as a local area network, a backbone network and/or the Internet, etc. I/O module 220 may also be operative to use a wired or wireless connection to connect to other components of system 200, e.g., camera 230, telemetry ECU 240, and/or shock sensor 250. It will be appreciated that in operation I/O module 220 may be implemented as a multiplicity of modules, where different modules may be operative to use different communication technologies. For example, a module providing mobile network connectivity may be used to connect to obstacle avoidance server 400, whereas a local area wired connection may be used to connect to camera 230, telemetry ECU 240, and/or shock sensor 250.

In accordance with embodiments described herein, camera 230, telemetry ECU 240, and shock sensor 250 represent implementations of sensor(s) 130 from FIG. 3A. It will be appreciated that camera 230, telemetry ECU 240, and/or shock sensor 250 may be implemented as integrated components of vehicle 100 (FIG. 3A) and may provide other functionality that is the interests of clarity is not explicitly described herein. As described hereinbelow, system 200 may use information about a current driving environment as received from camera 230, telemetry ECU 240, and/or shock sensor 250 to determine an appropriate driving policy for vehicle 100.

Autonomous driving manager 260 may be an application implemented in hardware, firmware, or software that may be executed by processing circuitry 210 to provide driving instructions to vehicle 100. For example, autonomous driving manager 260 may use images received from camera 230 and/or telemetry data received from telemetry ECU 240 to determine an appropriate driving policy for arriving at a given destination and provide driving instructions to vehicle 100 accordingly. It will be appreciated that autonomous driving manager 260 may also be operative to use other data sources when determining a driving policy, e.g., maps of potential routes, traffic congestion reports, etc.

As depicted in FIG. 3B, autonomous driving manager 260 comprises obstacle detector 265, obstacle predictor 262, and obstacle avoidance module 268. It will be appreciated that the depiction of obstacle detector 265, obstacle predictor 262, and obstacle avoidance module 268 as integrated components of autonomous driving manager 260 may be exemplary. The embodiments described herein may also support implementation of obstacle detector 265, obstacle predictor 262, and obstacle avoidance module 268 as independent applications in communication with autonomous driving manager 260, e.g., via I/O module 220.

Obstacle detector 265, obstacle predictor 262, and obstacle avoidance module 268 may be implemented in hardware, firmware, or software and may be invoked by autonomous driving manager 260 as necessary to provide input to the determination of an appropriate driving policy for vehicle 100. For example, obstacle detector 265 may be operative to use information from sensor(s) 130 (FIG. 3A), e.g., camera 230, telemetry ECU 240, and/or shock sensor 250 to detect obstacles in (or near) the driving path of vehicle 100, e.g., along (or near) roadway 20 (FIG. 3A). Obstacle predictor 262 may be operative to use obstacle information received from obstacle avoidance server 400 to predict the location of obstacles along or near roadway 20 before, or in parallel to their detection by obstacle detector 265. Obstacle avoidance module 268 may be operative to determine an appropriate driving policy based at least on obstacles detected/predicted (or not detected/predicted) by obstacle detector 265 and/or obstacle predictor 262.

Autonomous driving manager 260 may store obstacle information received from obstacle avoidance server 400 in obstacle database 270 for use by obstacle detector 265, obstacle predictor 262, and obstacle avoidance module 268 as described herein. It will be appreciated that obstacle information, including driving policies to avoid detected obstacles may also be stored in obstacle database 270 for use by obstacle detector 265, obstacle predictor 262, and obstacle avoidance module 268.

Reference is now made also to FIG. 3C which is a flowchart of an exemplary obstacle detection and avoidance process 300 (hereinafter also referred to as process 300) to be performed by system 200 during (or preceding) a driving session for vehicle 100 (FIG. 3A). The components of systems 100 and 200 will be referred to herein as per the reference numerals in FIGS. 3A and 3B.

In accordance with some embodiments described herein, system 200 may receive (step 310) an obstacle warning from obstacle avoidance server 400, e.g. obstacle information received from other vehicles 100 using system 10 and/or and other suitable sources as described with reference to FIG. 3A. It will be appreciated that the obstacle warning may be received by I/O module 220. Depending on the configuration of system 100, the obstacle warning may be received in a batch update process, either periodically and/or triggered by an event, e.g., when vehicle 100 is turned on, when vehicle 100 enters a new map area, when vehicle 100 enters an area with good wireless reception, etc. It will be appreciated that the obstacle warning received in step 310 may also include a description of the obstacle, e.g., a pothole.

Autonomous driving manager 260 may invoke obstacle predictor 262 to estimate (step 320) a location for the obstacle associated with the obstacle warning. It will be appreciated that, as discussed hereinabove, while the obstacle warning may include GPS coordinates for the obstacle, the coordinates may not be accurate enough to provide the location with sufficient precision to be of use in an automated. Furthermore, even if the coordinates for the obstacle, e.g., obstacle 40 (FIG. 3A) are accurate, vehicle 10 will likely be in movement and it may not be possible to accurately determine the GPS coordinates of vehicle 10 as it approaches the obstacle.

In accordance with embodiments described herein, the obstacle information in the obstacle warning may include landmarks to be used for calculating the obstacle location. For example, the location of the associated obstacle may be indicated in the obstacle warning as an offset from a local landmark, e.g., one or more of static reference points 30 (FIG. 3A). It will be appreciated that static reference points 30 may be visible from a greater distance than obstacle 40; a signpost typically extends vertically from the ground such that static reference points 30 may be captured by sensor(s) 130, e.g., camera 230, in advance of detection of obstacle 40. Obstacle predictor may then use static reference points 30 to predict a location for the associated obstacle 40 as an offset from the observed location of one or more static reference points 30 before sensor(s) 130 may actually provide an indication of the location. Distance from a given static reference point 30 may be estimated as a function of current speed for vehicle 10 and a frames-per-second setting for camera 230. Alternatively, or in addition, multiple static reference points may be used to locate obstacle 40 by triangulation, e.g., a distance from both static reference points 30A and 30B may be estimated and then used to predict the location of obstacle 40.

It will be appreciated that in some embodiments, sensor(s) 130 may be operative to provide an estimate for distance. For example, sensor(s) may be operative to use radar imagery, LIDAR, etc.

Autonomous driving manager 260 may invoke obstacle avoidance module 268 to enact (step 330) preventative measures to avoid colliding with, or running over, obstacle 40. Obstacle avoidance module 268 may enact the preventative measures in accordance with driving policies stored in obstacle database 270. It will be appreciated that the obstacle warning may include a description of obstacle 40, e.g., a pothole. Using the obstacle description and other available information (e.g., distance to obstacle 40 as estimated in step 320, current speed, road conditions, etc.), obstacle avoidance module 268 may select an appropriate driving policy from obstacle database 270 for implementation. For example, the selected driving policy may entail a reduction in speed, switching lanes (if possible under current conditions), swerving within a lane, etc. It will be appreciated that the driving policies in obstacle database may be may be pre-stored in obstacle database 270. It will further be appreciated that the driving policies may be derived based on supervised or unsupervised analysis of imagery from actual driving sessions. Some, or all, of the driving policies may also be defined manually.

In some cases, the selected driving policy may entail more extreme preventative measures. For example, obstacle 40 may be described in the obstacle warning as a very large obstacle that may effectively block traffic on roadway 20, e.g., a rockfall, or debris from a multi-vehicle accident. In such a case, the driving policy may entail actions such as, for example, pulling over on the shoulder, stopping in place, or changing routes.

Autonomous driving manager may invoke obstacle detector 265 to determine if an obstacle, e.g., obstacle 40, has been detected (step 340). For example, obstacle detector 265 may use imagery received from sensor(s) 130 to detect obstacle 40 in roadway 20.

It will be appreciated that in typical operation of process 300, steps 310-330 may not always be performed; autonomous driving system 200 may not always receive an obstacle warning in advance of encountering obstacle 40. For example, vehicle 100 may be the first vehicle associated with system 10 to encounter obstacle 40. It will therefore be appreciated that autonomous driving manager 260 may continuously execute process 300 in an attempt to detect and avoid obstacles as vehicle 100 progresses along roadway 20. Accordingly, step 340 may be performed independently of whether or not an obstacle warning has been received for a given area on roadway 20. It will, however, be appreciated, that in the event that an obstacle warning was indeed received, the relevant obstacle information may be used by obstacle detector 265 to determine whether or not the indicated obstacle, e.g., obstacle 40, is indeed in place on roadway 20 as indicated in the obstacle warning.

Alternatively, or in addition, obstacle detector 265 may be detected in response to information received from shock sensor 250. Shock sensor 250 may be a sensor operative to monitor the operation of the shock absorbers on vehicle 100. It will be appreciated that when running over a pothole, shock absorbers may absorb the ensuing shock and stabilize vehicle 100. It will also be appreciated that in some situations, potholes may not be detected until vehicle 100 actually drives over them. In such a situation, obstacle detector 265 may use information from shock sensor 250 to detect obstacle 40, albeit after vehicle 100 has already hit obstacle 40.

If obstacle 40 is detected in step 340, autonomous driving manager 260 may invoke obstacle avoidance module 268 to determine whether obstacle 40 is in the path of vehicle 100 (step 350). It will be appreciated that some obstacles 40 on roadway 20 may not be in the direct path of vehicle 100; some obstacles 40 may be in a different lane than vehicle 100. Additionally, if step 330 was performed, vehicle 100 may have already switched lanes in advance of obstacle 40 being detected in step 340. Furthermore, if obstacle 40 was detected using shock sensor 250, vehicle 100 may have already run over obstacle 40 such that the result of step 350 may be “no”. If obstacle 40 is not detected in step 340, process control may flow through to step 380.

If, as per the result of step 350, obstacle 40 is in the path of vehicle 100, obstacle avoidance module 268 may determine whether obstacle 40 is avoidable (step 360). It will be appreciated that obstacle 40 may not be detected in step 340 until vehicle 100 is very close. For example, visibility may be poor, obstacle 40 may be located immediately around a bend in roadway 20, obstacle 40 may be effectively camouflaged by its surroundings (e.g., some potholes are not distinguishable from a distance), etc. Accordingly, by the time obstacle 40 is detected, there may not be enough time to enact avoidance measures prior to impact. If, as per the result of step 350, obstacle 40 is not avoidable, process control may flow through to step 380.

If, as per the result of step 360, obstacle 40 is avoidable, obstacle avoidance module 268 may instruct vehicle 100 to avoid (step 370) obstacle 40. For example, obstacle avoidance module 268 may instruct vehicle 100 to straddle obstacle 40, swerve within the lane, or switch lanes altogether.

If, as per the result of step 360, obstacle 40 is not avoidable, obstacle avoidance module 268 may instruct vehicle 100 to minimize (step 365) impact with obstacle 40. For example, obstacle avoidance module 268 may instruct vehicle 100 to reduce speed and/or to swerve to avoid hitting the center of obstacle 40. Process control may then flow through to step 380.

Autonomous driving manager 380 may use I/O module 220 to send an obstacle report to obstacle avoidance server 400. The obstacle report may, for example, include telemetry data and sensor imagery that may be used by obstacle avoidance server 400 to determine a location for obstacle 40. For example, the obstacle report may include camera images (from camera 230) and telemetry data (from telemetry ECU 240) from a period staring a given number of seconds before obstacle 40 was detected in step 340. Alternatively, or in addition, the telemetry data and camera images may be from a period defined according to a physical distance from obstacle 40. The obstacle report may also include data from shock sensor 250 where applicable.

Depending on the configuration of systems 100 and 200, step 380 may be performed in real time or near real time after obstacle 40 is detected in step 340. Alternatively, or in addition, step 380 may be performed in a periodic and/or event triggered batch process.

It will be appreciated that obstacle reports may also be provided for “no” results in process 300. If obstacle 40 is not detected in step 340, an obstacle report may be provided to obstacle avoidance server 400 indicating that an obstacle was not detected at the associated location. It will be appreciated that in some cases, a “no” result in step 340 may indicate that an obstacle associated with an obstacle warning may no longer be found on roadway 20, e.g., the pothole may have been filled in. In other cases, where an obstacle warning was not received, the “no” result may indicate that the default condition, e.g., “no obstacles”, is in effect for roadway 20.

Reference is now made to FIG. 4 which is a block diagram of an exemplary obstacle avoidance server 400 (hereinafter also referred to as server 400), constructed and implemented in accordance with embodiments described herein. Server 400 comprises processing circuitry 410, input/output (I/O) module 420, obstacle avoidance manager 460, and obstacle database 470. Obstacle avoidance manager 460 may be instantiated in a suitable memory for storing software such as, for example, an optical storage medium, a magnetic storage medium, an electronic storage medium, and/or a combination thereof.

Processing circuitry 410 may be operative to execute instructions stored in memory (not shown). For example, processing circuitry 410 may be operative to execute obstacle avoidance manager 260. It will be appreciated that processing circuitry 410 may be implemented as a central processing unit (CPU), and/or one or more other integrated circuits such as application-specific integrated circuits (ASICs), field programmable gate arrays (FPGAs), full-custom integrated circuits, etc., or a combination of such integrated circuits. It will similarly be appreciated that server 400 may comprise more than one instance of processing circuitry 410. For example, one such instance of processing circuitry 410 may be a special purpose processor operative to execute obstacle avoidance manager 460 to perform some, or all, of the functionality of server 400 as described herein.

I/O module 420 may be any suitable communications component such as a network interface card, universal serial bus (USB) port, disk reader, modem or transceiver that may be operative to use protocols such as are known in the art to communicate either directly, or indirectly, with other elements of system 10 (FIG. 3A) such as, for example, system 200 (FIG. 3B). As such, I/O module 420 may be operative to use a wired or wireless connection to connect to system 200 via a communications network such as a local area network, a backbone network and/or the Internet, etc. It will be appreciated that in operation I/O module 220 may be implemented as a multiplicity of modules, where different modules may be operative to use different communication technologies. For example, a module providing mobile network connectivity may be used to connect wirelessly to one instance of system 200, e.g., one vehicle 100 (FIG. 3A), whereas a local area wired connection may be used to connect to a different instance of system 100, e.g., a different vehicle 100.

Obstacle avoidance manager 460 may be an application implemented in hardware, firmware, or software that may be executed by processing circuitry 410 to provide obstacle warnings and/or driving policies to vehicles 100. For example, obstacle avoidance manager 460 may use obstacle information in obstacle reports received from vehicles 100 to provide obstacle warnings in real time, near real time, and/or batch mode to vehicles 100 in system 10 (FIG. 3A). It will be appreciated that obstacle avoidance manager 460 may also be operative to use other data sources when detecting obstacles and determining driving policies, e.g., maps of potential routes, traffic congestion reports, etc.

As depicted in FIG. 4, obstacle avoidance manager 460 comprises obstacle extractor 462, obstacle categorizer 464, obstacle policy manager 466, and obstacle timer 468. It will be appreciated that the depiction of obstacle extractor 462, obstacle categorizer 464, obstacle policy manager 466, and obstacle timer 468 as integrated components of obstacle avoidance manager 460 may be exemplary. The embodiments described herein may also support implementation of obstacle extractor 462, obstacle categorizer 464, obstacle policy manager 466, and obstacle timer 468 as independent applications in communication with obstacle avoidance manager 460, e.g., via I/O module 420.

Obstacle extractor 462, obstacle categorizer 464, obstacle policy manager 466, and obstacle timer 468 may be implemented in hardware, firmware, or software and may be invoked by obstacle avoidance manager 460 as necessary to provide obstacle warnings and associated driving policies to vehicles 100. For example, obstacle extractor 462 may be operative to extract obstacle information from obstacle reports received from vehicles 100. Obstacle categorizer 464 may be operative to use categorize obstacles in terms of danger level and/or persistence characteristics. Obstacle policy manager 466 may be operative to determine an appropriate driving policy for a given obstacle or category of obstacle. Obstacle timer 468 may be operative to set an expiration timer for obstacles according to their associated obstacle categories.

Obstacle avoidance manager 460 may store obstacle information received from vehicles 100 in obstacle database 270 for use by obstacle extractor 462, obstacle categorizer 464, obstacle policy manager 466, and obstacle timer 468 as described herein. It will be appreciated that obstacle database 470 may also be used to store obstacle information, including driving policies, derived by obstacle extractor 462, obstacle categorizer 464, obstacle policy manager 466, and obstacle timer 468.

Reference is now made also to FIG. 5 which is a flowchart of an exemplary obstacle detection and warning process 500 (hereinafter also referred to as process 500) to be performed by server 400 to support driving sessions for vehicles 100 (FIG. 3A). The components of systems 10 and 200 will be referred to herein as per the reference numerals in FIGS. 3A and 3B. 1 and 2. Similarly, the components of server 400 will be referred to herein as per the reference numerals in FIG. 4.

In accordance with some embodiments described herein, server 400 may receive (step 510) an obstacle report from vehicle 100. It will be appreciated that the obstacle warning may be received by I/O module 420. Depending on the configuration of system 10, the obstacle report may be received in real time or near real time after vehicle 100 encounters obstacle 40. Alternatively, or in addition, the obstacle report may be received in a batch update process, either periodically and/or triggered by an event, e.g., when vehicle 100 is turned on, when vehicle 100 enters a new map area, when vehicle 100 enters an area with good wireless reception, etc. It will be appreciated that the obstacle report received in step 510 may also include an image of obstacle 40.

It will be appreciated that as described hereinabove with respect to process 300 (FIG. 3C), a given obstacle report may not necessarily be triggered by detection of an obstacle by vehicle 100. The obstacle report may represent a periodic upload of GPS data, camera images, telemetry data, and/or other sensor data that may not be associated with a specific detection of an obstacle. The obstacle report may also be associated with a non-detection of an obstacle that was otherwise indicated by a previously issued obstacle warning. Furthermore, as described with respect to FIG. 3A, raw driving data from manually and/or semi-autonomously driven vehicles may also be provided to server 400. In accordance with embodiments described herein, such raw driving data may also be input to process 500 along with obstacle reports.

Accordingly, obstacle avoidance server 400 may invoke obstacle extractor 462 to analyze received obstacle reports and/or raw driving data to determine if a given obstacle report (or raw driving data) includes a reported obstacle (step 520), i.e., where the obstacle report specifically addresses an obstacle, whether it was detected by vehicle 100 and/or it was included in a previous obstacle warning.

Obstacle extractor 462 may determine if the obstacle report or raw driving data includes at least a reference to a reported obstacle (step 520). It will be appreciated that as described with respect to process 300, an obstacle report may be sent in response to an obstacle warning, even if the associated obstacle is not actually detected by autonomous driving manager 360. Such an obstacle report may still include a reference to the reported obstacle. If the obstacle report or raw driving data includes at least a reference to a reported obstacle (step 520) processing may continue with step 530.

If the obstacle report or raw driving data does not include a reference to a reported obstacle (step 520), obstacle extractor 462 may process (step 525) the obstacle report (or raw driving data) to extract obstacle information for obstacles. For example, obstacle extractor 464 may be operative to detect an obstacle based on an image of an obstacle, where the image may be matched to a reference image of an obstacle stored in obstacle database 470. Alternatively, or in addition, obstacle extractor 464 may be operative to detect an obstacle based on telemetry data, where certain driving patterns may be indicative of a driver avoiding an obstacle. Alternatively, or in addition, obstacle extractor 464 may be operative to detect an obstacle based on analysis of shock absorber data, where shock events may be indicative of a vehicle running over a pothole. It will be appreciated that the obstacle information extracted in step 525 may also include reference data that may be used to provide a location for the extracted obstacle, e.g., GPS coordinates, offsets from fixed reference points 30, radar imagery, etc.

If one or more obstacles are extracted (step 529) processing control may flow through to step 530. Otherwise, processing control may return to step 510.

Obstacle avoidance manager 460 may determine whether a reported obstacle or an extracted obstacle is a “new” obstacle (step 530), i.e., whether the associated obstacle is already known to server 400 from a previous iteration of process 300. If the obstacle is a new obstacle, obstacle avoidance manager 460 may invoke obstacle categorizer 464 to categorize the obstacle. Obstacle categorizer 464 may categorize the obstacle based on a level of danger it may represent to vehicle 100. For example, if the obstacle is a relatively shallow pothole, obstacle categorizer may assign it to a low-risk category. If the obstacle is a patch of ice, obstacle categorizer may assign it to a medium-risk category. If the obstacle is a large slab of concrete, obstacle categorizer may assign it to a high-risk category. It will be appreciated that the examples provided herein for obstacles and their associated categories are not limiting; the embodiments described herein may support other combinations of obstacles and categories.

Obstacle categorizer 464 may also be operative to categorize obstacles based on their expected persistence. For example, a pothole may be a persistent obstacle; it may be expected to remain on roadway 20 for weeks, months, or even years after first being observed. Debris from a traffic accident may be a temporary obstacle; it may be expected to be cleared from roadway 20 within hours or perhaps a day or two after first being observed. Some obstacles may be recurring in nature; such obstacles may be observed intermittently in the same location. For example, puddles or ice patches may tend to recur in the same locations based on the topography of roadway 20. Some obstacles may have hybrid persistence characteristics. For example, a pothole may also tend to fill with rain/slow and freeze during the winter. The pothole itself may be of a persistent nature, whereas the resulting ice patch (or puddle) may be of a recurring nature. Obstacle categorizer 464 may categorize (step 535) the obstacle accordingly, e.g. as persistent, temporary, recurring, or hybrid.

Obstacle avoidance manager 460 may invoke obstacle policy manager 466 to set (step 539), based at least on the obstacle's category, an appropriate driving policy for avoiding (or at least minimizing the risks associated with) the obstacle.

If, per step 530, the obstacle is not a new obstacle, obstacle avoidance manager 460 may use obstacle information stored in obstacle database 470 to determine whether the current obstacle information is consistent with previously observed obstacle information (step 540). For example, a pothole may expand in size and/or fill with water/ice compared to a previous observation. Debris from a traffic accident may shift or be partially removed. If the current obstacle information is inconsistent with the stored obstacle information, obstacle avoidance manager 460 may invoke obstacle categorizer 464 to update (step 545) the category or categories assigned in step 535, and obstacle policy manager 466 to update (step 549) the driving policy set in step 539.

As described hereinabove, even though a given obstacle report may be associated with a previously issued obstacle warning, in some cases the obstacle report may not include sensor data indicative of an observation of the associated obstacle. Vehicle 100 may receive an obstacle warning, but by the time vehicle 100 reaches the location indicated in the obstacle warning, the associated obstacle may no longer be there. For example, based on obstacle information received by obstacle avoidance server 400, obstacle avoidance manager 460 may determine that debris from traffic accident may be at a given location on roadway 20, and send an obstacle warning to vehicle 100 for the given location. However, the debris may be cleared by the time that vehicle 100 reaches the given location. As described with reference to process 300, in such a case, vehicle 100 may provide an obstacle report to server 400 even though it did not actually detect the obstacle associated with the obstacle warning.

Accordingly, obstacle avoidance manager 460 may determine if an obstacle associated with the obstacle report was actually detected by autonomous driving system 200 or obstacle extractor 462 (step 550). If an obstacle was detected per step 550, obstacle avoidance manager 460 may use obstacle timer 468 to set (step 555) a timer for the detected obstacle. The timer may be set according to an anticipated duration for the obstacle. For example, if the obstacle is debris from a traffic accident, the timer may be set to a number of hours; for a pothole the timer may be set for a number of weeks or months.

It will be appreciated that the detected obstacle from step 550 may already have had a timer set in a previous iteration of process 500, e.g., the obstacle report is associated with a known obstacle for which an obstacle warning was previously generated and sent to vehicle(s) 100. In such a case, the timer may be reset to extend the anticipated duration of the obstacle per a more recent observation. For example, if the obstacle was debris from a traffic accident, the timer may have originally been set for two hours. If an obstacle report is received ninety minutes later indicating that the debris is still there, obstacle avoidance manager 460 may reset the timer to update the anticipated duration based on the latest information (i.e., the recent obstacle report).

If an obstacle associated with the obstacle report was not detected by autonomous driving system 200 or obstacle extractor 462 (step 550), obstacle avoidance manager 460 may determine if the obstacle warning has expired (step 560) according to the timer (re)set in step 555. If the timer has expired, obstacle avoidance manager 460 may remove (step 465) the obstacle from a warning list of currently relevant obstacle warnings. Otherwise, processing control may continue to step 570.

Obstacle avoidance manager 460 may then update (step 570) the warning list as necessary e.g., adding obstacle warnings, modifying driving policies, removing obstacle warnings, etc. Obstacle avoidance manager 460 may then send (step 580) the obstacle warning(s) from the warning list to vehicle(s) 100. Depending on the configuration of systems 10, 200, and 400, step 580 may be performed in real time or near real time after step 570. Alternatively, or in addition, step 580 may be performed in a periodic and/or event triggered batch process.

Process control may then return to step 510. It will be appreciated that in some embodiments, steps 520-570 (or combinations, thereof) may be performed iteratively, e.g., if multiple obstacles are extracted from raw driving data.

Obstacle Detection Based on Visual and Non-Visual Information

Obstacles may be viewed as elements that introduce changes in the behavior of a vehicle. Obstacles may be automatically detected by processing images obtained while a vehicle performs maneuvers that are suspected as being obstacle avoidance maneuvers.

FIG. 6 illustrates a method 1600 executed by computerized system for detecting obstacles. The computerized system may be located outside a vehicle, may be distributed between different vehicles, and the like.

Method 1600 may start by step 1610 of receiving, from a plurality of vehicles, and by an I/O module of a computerized system, visual information acquired during executions of vehicle maneuvers that are suspected as being obstacle avoidance maneuvers.

Step 1610 may also include receiving behavioral information regarding behavior of the plurality of vehicles, during an execution of the maneuvers that are suspected as being obstacle avoidance maneuvers. The behavioral information may represent the behavior (speed, direction of propagation, and the like) of the entire vehicle and/or may represent the behavior of parts or components of the vehicle (for example damping a shock by a shock absorber, slowing the vehicle by the breaks, turns of the steering wheel, and the like), and/or may represent the behavior of the driver (the manner in which the driver drives), and the like.

A determination of what constitutes a vehicle maneuver suspected as being an obstacle avoidance maneuver may be detected in a supervised manner or in a unsupervised manner. For example—when in supervised manner method 1600 may include receiving examples of maneuvers that are tagged as obstacle avoidance maneuvers.

Such a maneuver may be an uncommon maneuver in comparison to at least one out of (a) maneuvers of the same vehicle, (b) maneuvers performed at the same location by other drivers, (c) maneuvers performed in other places by other drivers, and the like. The maneuver may be compared to other maneuvers at the same time of day, at other times of day, at the same date, at other dates and the like.

The vehicle maneuver suspected as being an obstacle avoidance maneuver may be detected based on behavioral information that may be obtained from one or more sensors such as an accelerometer, a wheel speed sensor, a vehicle speed sensor, a brake sensor, a steering wheel sensor, a shock absorber sensor, an engine sensor, a driver sensor for sensing one or more physiological parameter of the driver (such as heart beat), or any other sensor—especially any other non-visual sensor.

The vehicle maneuver suspected as being an obstacle avoidance maneuver may involve a change (especially a rapid change) in the direction and/or speed and/or acceleration of the vehicle, a deviation from a lane, a deviation from a previous driving pattern followed at attempt to correct the deviation, and the like.

For example—when applying supervised learning of obstacles—the computerized system may be fed with examples of vehicle behaviors when bypassing known obstacles, when approaching a significant bump, and the like.

For example—the determining may include ruling out common behaviors such as stopping in front of a red traffic light.

For example—a fast and significant change in the speed and direction of a vehicle at a linear road path may indicate that the vehicle encountered an obstacle.

The visual information may be raw image data (such as images) acquired by a visual sensor of the vehicle, processed images, metadata or any other type of visual information that represents the images acquired by visual sensor.

The visual information may include one or more robust signatures of one or more images acquired by one or more visual sensors of the vehicle. A non-limiting example of a robust signature is illustrated in U.S. Pat. No. 8,326,775 which is incorporated herein by reference.

Step 1610 may be followed by step 1620 of determining, based at least on the visual information, at least one visual obstacle identifier for visually identifying at least one obstacle.

The determining may include clustering visual information based on the objects represented by the visual information, and generating a visual obstacle identifier that represents a cluster. The clusters may be linked to each other and a visual obstacle identifier may represent a set of linked clusters.

The determining may include filtering out objects that are represented in the visual information based on a frequency of appearance of the objects in the visual information. For example—the road may appear in virtually all images and may be ignored.

The clustering may or may not be responsive to the behavior of the vehicle during the executions of vehicle maneuvers that are suspected as being obstacle avoidance maneuvers.

The visual obstacle identifier may be a model of the obstacle, a robust signature of the obstacle, configuration information of a neural network related to the obstacle.

A model of the obstacle may define one or more parameters of the obstacle such as shape, size, color of pixels, grayscale of pixels, and the like.

The configuration information of a neural network may include weights to be assigned to a neural network. Different neural networks may be used to detect different obstacles.

The visual obstacle identifier for visually identifying an obstacle may identify a group of obstacles to which the obstacle belongs. For example—the visual obstacle identifier may identify a concept. See, for example, U.S. Pat. No. 8,266,185 which is incorporated herein by reference.

A visual obstacle identifier that identifies an obstacle may include at least one out of severity metadata indicative of a severity of the obstacle, type metadata indicative of a type of the obstacle (for example—pothole, tree, bump), size metadata indicative of a size of the obstacle, timing metadata indicative of a timing of an existence of the obstacle (for example—persistent, temporary, recurring), and location metadata indicative of a location of the obstacle. See, for example severity metadata 1851, type metadata 1852, size metadata 1853, timing metadata 1854 and location metadata 1855 of FIG. 7.

Step 1620 may be followed by step 1630 of responding to the outcome of step 1620. For example—step 1630 may include transmitting to one or more of the plurality of vehicles, the at least one visual obstacle identifier. Additionally or alternatively, step 1630 may include populating an obstacle database. The visual obstacle identifier may be included in an obstacle warning.

The behavioral information is obtained by non-visual sensors of the plurality of vehicles.

Step 1630 may also include verifying, based on at least the visual information, whether the maneuvers that are suspected as being obstacle avoidance maneuvers are actually obstacle avoidance maneuvers.

The verification may include detecting at least a predefined number of indications about a certain obstacle.

The verification may include receiving information from another source (for example input from drivers, information provided from the police, department of transportation or other entity responsible to the maintenance of roads, regarding the existence of obstacles.

Step 1630 may be followed by step 1640 of transmitting to one or more of the plurality of vehicles, verification information indicative of the maneuvers that are actually obstacle avoidance maneuvers. This may help the vehicles to ignore maneuvers that were suspected as obstacle avoidance maneuvers—which are not actual obstacle avoidance maneuvers.

Method 1600 enables a vehicle that receives a visual obstacle identifier to identify an obstacle of known parameters—even if the vehicle is the first one to image that obstacle. Method 1600 may be executed without human intervention and may detect obstacles without prior knowledge of such obstacles. This provides a flexible and adaptive method for detecting obstacles. The visual information and/or the visual obstacle identifier may be very compact—thereby allowing the learning of obstacles and afterwards detecting obstacles to be executed using a relatively small amount of computational and/or storage and/or communication resources.

It should be noted that different vehicle may perform obstacle avoidance maneuvers that may differ from each other by duration, vehicle behavior and the like. An example is illustrated in FIGS. 8-10 in which first, second and third vehicles perform different obstacle avoidance maneuvers and acquire different numbers of images during said obstacle avoidance maneuvers.

FIG. 8 illustrates a first vehicle (VH1) 1801 that propagates along a road 1820. First vehicle 1801 performs a maneuver 1832 suspected as being a obstacle avoidance maneuver when encountered with obstacle 1841. Maneuver 1832 is preceded by a non-suspected maneuver 1831 and is followed by another non-suspected maneuver 1833.

First vehicle 1801 acquires a first plurality (N1) of images I1(1)-I1(N1) 1700(1,1)-1700(1,N1) during obstacle avoidance maneuver 1832.

Visual information V1(1)-V1(N1) 1702(1,1)-1702(1,N1) is sent from first vehicle 1801 to computerized system (CS) 1710 via network 1720.

The visual information may be the images themselves. Additionally or alternatively, first vehicle processes the images to provide a representation of the images.

First vehicle 1801 may also transmit behavioral information B1(1)-B1(N1) 1704(1,1)-1704(1,N1) that represents the behavior of the vehicle during maneuver 1832.

FIG. 9 illustrates a second vehicle (VH2) 1802 that propagates along a road 1820. Second vehicle 1802 performs a maneuver 1833 suspected as being a obstacle avoidance maneuver when encountered with obstacle 1841. Maneuver 1832 is preceded by a non-suspected maneuver and is followed by another non-suspected maneuver.

Second vehicle 1802 acquires a second plurality (N2) of images I2(1)-I2(N2) 1700(2,1)-1700(2,N2) during maneuver 1833.

Visual information V2(1)-V2(N2) 1702(2,1)-1702(2,N2) is sent from second vehicle 1802 to computerized system (CS) 1710 via network 1720.

The visual information may be the images themselves. Additionally or alternatively, second vehicle processes the images to provide a representation of the images.

Second vehicle 1802 may also transmit behavioral information (not shown) that represents the behavior of the vehicle during maneuver 1832.

FIG. 10 illustrates a third vehicle (VH2) 1803 that propagates along a road. Third vehicle 1803 performs a maneuver 1834 suspected as being an obstacle avoidance maneuver when encountered with obstacle 1841. Maneuver 1834 is preceded by a non-suspected maneuver and is followed by another non-suspected maneuver.

Third vehicle 1803 acquires a third plurality (N3) of images I3(1)-I3(N3) 1700(3,1)-1700(3,N3) during maneuver 1834.

Visual information V3(1)-V3(N3) 1702(3,1)-1702(3,N3) is sent from third vehicle 1803 to computerized system (CS) 1710 via network 1720.

The visual information may be the images themselves. Additionally or alternatively, third vehicle processes the images to provide a representation of the images.

Third vehicle 1803 may also transmit behavioral information (not shown) that represents the behavior of the vehicle during maneuver 1832.

FIG. 11 illustrates a method 1900 for detecting obstacles. The method may be executed by a vehicle.

Method 1900 may start by steps 1910 and 1920.

Step 1910 may include sensing, by a non-visual sensor of a vehicle, a behavior of a vehicle. The non-visual sensor may be an accelerometer, a shock absorber sensor, a brakes sensor, and the like.

Step 1920 may include acquiring, by a visual sensor of the vehicle, images of an environment of the vehicle. Step 1920 may continue even when such a suspected maneuver is not detected.

Steps 1920 and 1930 may be followed by step 1930 of determining, by a processing circuitry of the vehicle, whether the behavior of the vehicle is indicative of a vehicle maneuver that is suspected as being an obstacle avoidance maneuver.

The vehicle maneuver suspected as being an obstacle avoidance maneuver may be detected based on behavioral information that may be obtained from one or more sensors such as an accelerometer, a wheel speed sensor, a vehicle speed sensor, a brake sensor, a steering wheel sensor, an engine sensor, a shock absorber sensor, a driver sensor for sensing one or more physiological parameter of the driver (such as heart beat), or any other sensor—especially any other non-visual sensor.

The vehicle maneuver suspected as being an obstacle avoidance maneuver may involve a change (especially a rapid change) in the direction and/or speed and/or acceleration of the vehicle, a deviation from a lane, a deviation from a previous driving pattern followed at attempt to correct the deviation, and the like.

Step 1930 may be followed by step 1940 of processing the images of the environment of the vehicle obtained during a vehicle maneuver that is suspected as being the obstacle avoidance maneuver to provide visual information.

The visual information may be raw image data (such as images) acquired by a visual sensor of the vehicle, processed images, metadata or any other type of visual information that represents the images acquired by visual sensor.

The visual information may include one or more robust signatures of one or more images acquired by one or more visual sensors of the vehicle. A non-limiting example of a robust signature is illustrated in U.S. Pat. No. 8,326,775 which is incorporated herein by reference.

Step 1940 may be followed by step 1950 of transmitting the visual information to a system that is located outside the vehicle.

Method 1900 may also include step 1960 of receiving at least one visual obstacle identifier from a remote computer.

Method 1900 may include step 1970 receiving verification information indicative of whether the maneuver that was suspected being an obstacle avoidance maneuver is actually an obstacle avoidance maneuver. This may be used in the vehicle during step 1930.

Methods 1900 and 1600 illustrate a learning process. The products of the learning process may be used to detect obstacles—during an obstacle detection process that uses the outputs of the learning process.

The obstacle detection process may include detecting newly detected obstacles that were not detected in the past.

The learning process may continue during the obstacle detection process or may terminate before the obstacle detection process.

FIG. 12 illustrates method 2100 for detecting an obstacle.

Method 2100 may start by step 2110 of receiving, by an I/O module of a vehicle, a visual obstacle identifier for visually identifying an obstacle; wherein the visual obstacle identifier is generated based on visual information acquired by at least one visual sensor during an execution of at least vehicle maneuver that is suspected as being an obstacle avoidance maneuver. Thus step 2110 may include receiving outputs of the learning processes of method 1600.

Method 2100 may also include step 2120 of acquiring, by a visual sensor of the vehicle, images of an environment of the vehicle.

Step 2120 may be followed by step 2130 of searching, by a processing circuitry of the vehicle, in the images of the environment of the vehicle for an obstacle that is identified by the visual obstacle identifier.

If finding such an obstacle then step 2130 may be followed by step 2140 of responding, by the vehicle, to a detection of an obstacle.

The responding may be based on a driving policy, may be responsive to the mode of controlling the vehicle (any level of automation—starting from fully manual control, following by partial human intervention and ending with fully autonomous).

For example—the responding may include generating an alert perceivable by a human driver of the vehicle, sending an alert to a computerized system located outside the vehicle, handing over the control of the vehicle to a human driver, handing over a control of the vehicle to an autonomous driving manager, sending an alert to an autonomous driving manager, performing an obstacle avoidance maneuver, determining whether the obstacle is a newly detected obstacle, informing another vehicle about the obstacle, and the like.

Determining a Location of a Vehicle

A vehicle, especially but not limited to an autonomous vehicle, has to known its exact location in order to propagate in an efficient manner. For example—the vehicle may receive an obstacle warning that includes the location of the obstacle.

Responding to the obstacle warning may require to know the exact location of the vehicle. Furthermore—an autonomous driving operation may require a knowledge of the exact location of the vehicle or may be executed in a more optimal manner if the exact location of the vehicle is known. In general, the processing burden associated with autonomous driving decision may be lowered when an autonomous driving manager is aware of the location of the vehicle.

Global positioning systems (GPS) are inaccurate and cannot be solely used for locating the exact location of a vehicle.

There is a growing need to determine the exact location of the vehicle in an efficient manner.

There is a provided an efficient and accurate method for determining the exact location of a vehicle.

The method is image-based in the sense that it is based on images acquired by the vehicle and on a comparison between the acquired images and reference information that was acquired at predefined locations.

The determining of the actual location of the vehicle is of a resolution that is smaller than a distance between adjacent reference images. For example—the resolution may be a fraction (for example between 1-15%, between 20-45%, of between 5-50%) of the distance between adjacent reference images. In the following example it is assumed that the distance between adjacent reference images is about 1 meter and the resolution is about 10 to 40 centimeters.

FIG. 13 illustrates method 1300 for determining a location of a vehicle. receiving reference visual information that represents multiple reference images acquired at predefined locations.

Method 1300 may start by step 1310 of receiving reference visual information that represents multiple reference images acquired at predefined locations.

The multiple reference images may be taken within an area in which the vehicle is located. The area may be of any size and/or dimensions. For example, the area may include a part of a neighborhood, a neighborhood, a part of a city, a city, a part of a state, a state, a part of a country, a country, a part of a continent, a continent, and even the entire world.

A computerized system and the vehicle may exchange reference visual information in real-time (or near real-time) and/or in a burst mode, where the reference visual information may be provided either periodically and/or when conditions are suitable for a data exchange between the computerized system and the vehicle. For example, a vehicle may be configured to perform regular uploads/downloads of reference visual information when the engine is turned on (or off, with a keep alive battery function to facilitate communication with the computerized system). Alternatively, or in addition, the vehicle may be configured to perform such uploads/downloads when stationary (e.g., parked, or waiting at a traffic light) and a sufficiently reliable wireless connection is detected. Alternatively, or in addition, the uploads/downloads may be triggered by location, where the reference visual information may be downloaded from the computerized system when the vehicle enters an area for which it does not have up-to-date reference visual information.

The reference visual information may include the reference images themselves or any other representation of the reference images.

The reference information may include robust signatures of reference images. The robust signature may be robust to at least one out of noise, lighting conditions, rotation, orientation, and the like—which may provide a robust location detection signatures. Furthermore—using robust signatures may simplify the acquisition of the reference images—as there is no need to acquired reference images in different lighting conditions or other conditions to which the robust signature is indifferent.

The comparison between the reference information and information from the acquired image may be even more efficient when the reference visual information and information regarding the acquired image is represented in a cortex representation.

A cortex representation of an image is a compressed representation of the visual information that is generated by applying a cortex function that include multiple compression iteration. A signature of an image may be a map of firing neurons that fire when a neural network is fed with an image. The map undergoes multiple compression iterations during which popular strings are replaced by shorter representations.

Method 1300 may also include step 1320 of acquiring, by a visual sensor of the vehicle, an acquired image of an environment of the vehicle. It should be noted that multiple images may be acquired by the vehicle—but for simplicity of explanation the following text will refer to a single image. The vehicle may repetitively determine its location—and multiple repetitions of steps 1320, 1330 and 1340 may be repeated multiple times—for different images.

Step 1330 may include generating, based on the acquired image, acquired visual information related to the acquired image.

The location determination may be based on visual image regarding static obstacles. Accordingly—each one of the reference visual information and the acquired visual information may include static visual information related to static objects.

The extraction of the static visual information may include reducing from the visual information any visual information that is related to dynamic objects. The extraction of the static information may or may not require object recognition.

The acquired visual information may include acquired static visual information that may be related to at least one static object located within the environment of the vehicle.

Step 1330 may be followed by step 1340 of searching for a selected reference image out of the multiple reference images. The selected reference image may include selected reference static visual information that best matches the acquired static visual information.

Step 1340 may be followed by step 1350 of determining an actual location of the vehicle based on a predefined location of the selected reference image and to a relationship between the acquired static visual information and to the selected reference static visual information.

The determining of the actual location of the vehicle may be of a resolution that may be smaller than a distance between the selected reference image and a reference image that may be immediately followed by the selected reference image.

The determining of the actual location of the vehicle may include calculating a distance between the predefined location of the selected reference image and the actual location of the vehicle based on a relationship between at least one value of a size parameter of the at least one static object within the selected reference image and at least one value of the size parameter of the at least one static object within the acquired image.

The determining of the actual location of the vehicle may include determining whether the actual location of the vehicle precedes the predefined location of the selected reference image or precedes the predefined location of the selected reference image.

The method may include subtracting the first distance from the predefined location of the selected reference image when determining that the actual location of the vehicle precedes the predefined location of the selected reference image.

The method may include adding the first distance from the predefined location of the selected reference image when determining that the actual location of the vehicle follows the predefined location of the selected reference image.

Each predefined location may be associated with a single reference image.

Alternatively, each reference image may be associated with multiple images that differ from each other by scale. The images that differ from each other by scale may represent different relative distances between the vehicle and the predefined image—larger scale may be related to smaller distances.

Using images of different scale (or visual information related to images of different scale) may reduce the computational load associated with location determination—as it may eliminate the need to manipulate a single reference image to find an exact math that may determine the distance between the vehicle and the reference location.

Using visual information related to images of different scale may also assist when The visual information is a lossy representation of the images in which a representation of an object of certain scale cannot be easily generated based on a representation of the same object of another scale. The visual information may be a signature of an image may be a map of firing neurons that fire when a neural network is fed with an image. See, for example, the signature of U.S. Pat. Nos. 8,326,775 and 8,312,031, assigned to common assignee, which are hereby incorporated by reference.

FIG. 14 illustrates an example of a vehicle VH1 1402 that acquires an image I(k) 1404 and tries to determine its location based on analysis of visual information it generates from image I(k) and multiple reference images RI(1,1)-RI(j,1) 1402(1)-1402(j) and RI(j+1,1)-RI(j) 1402(j+1,1)-1402(J,1). The front of the vehicle is positioned between the predefined locations associated with reference images RI(j) and RI(j+1)—and one of these images should be associated with reference visual information that best matches the visual information associated with image I(k). The distance between consecutive predefined locations is DRI 1422—and the location resolution is finer than DRI.

FIG. 15 illustrates an example of a vehicle VH1 1402 that acquires an image I(k) 1404 and tries to determine its location based on analysis of visual information (1406) it generates from image I(k) and visual information related to reference images. In FIG. 15 each predefined location is associated with a plurality of reference images—with a plurality (p) of reference visual information per reference location—1408(1,1)-1408(j,1), 1408(j+1,1)-1408(J,1), 1409(1,p)-1409(j,p), and 1409(j+1,p)-1409(J,p).

The front of the vehicle is positioned between the j′th and (j+1)′th predefined locations and the vehicle may determine the best matching predefined location and also the distance from the predefined locations based on the p different reference visual information associated with one of these predefined locations.

FIGS. 17 and 18 illustrate an example of an image and static visual information.

FIG. 17 illustrates an example of an image acquired by a vehicle. The image shows two lanes 1411 and 1412 of a road, right building 1413, vehicles 1414 and 1415, first tree 1416, table 1417, second tree 1418, cloud 1419, rain 1420, left building 1421, shrubbery 1422, child 1423 and ball 1424.

The dynamic objects of this image include vehicles 1414 and 1415, cloud 1419, rain 1420, shrubbery 1422, child 1423 and ball 1424. These dynamic objects may be represented by visual information (such as signatures) that describe them as dynamic—and visual information related to these dynamic objects may be removed from the visual information of the images to provide static visual information.

FIG. 18 illustrates the removal (blocks 1432) of dynamic visual information from the visual information 1430 to provide static visual information 1431.

Visual information 1430 may be a map of firing neurons of a network that fired when the neural network was fed with image I(k)—or may be a compressed representation (for example a cortex representation) of such map.

Triggering Human Intervention

FIG. 19 illustrates method 1450.

Method 1450 may be aimed to find at least one trigger for human intervention in a control of a vehicle.

Method 1450 may start by step 1452 of receiving, from a plurality of vehicles, and by an I/O module of a computerized system, visual information acquired during situations that are suspected as situations that require human intervention in the control of at least one of the plurality of vehicles.

Step 1452 may be followed by step 1456 of determining, based at least on the visual information, the at least one trigger for human intervention. The at least one trigger is represented by trigger visual information.

Step 1456 may include at least one out of

Determining a complexity of the situation. Determining a danger level associated with the situation. Determining in response to statistics of maneuvers executed by different vehicles during a same situation that is suspected as a situation that requires human intervention in the control of at least one of the plurality of vehicles. Determining based on generated or received movement information of entities included in the visual information. The movement information may represent entity movement functions of the entities. The determining may include estimating, based on the entity movement functions, a future movement of the entities.

The determining of step 1456 may be executed in an unsupervised manner or a supervised manner.

The determining of step 1456 is responsive to at least one human intervention policy of the at least one vehicle. The human intervention policy of a vehicle may differ from the human intervention policy of another vehicle.

A human intervention policy of a vehicle may define certain criteria human intervention such as the danger level of a situation, the complexity of the situation (especially complexity of maneuvers required to overcome the situation), the potential damage that may result to the vehicle, driver or surroundings due to the situation. A human intervention policy of a vehicle may also define certain situations that require human intervention—such as specific locations (for example near a cross road of a school), or specific content (near a school bus), and the like.

A trigger may be determined during step 1456 when the situation or one of the attributes of the situation (danger level, location, combination of location and time) match those of the human intervention policy.

Situations that are suspected as requiring human intervention in the control of a vehicle may be viewed as situations that are complex and/or dangerous and/or require changes in the behavior of a vehicle. These situations may result from an obstacle or may result from other reasons.

Situations that are suspected as requiring human intervention in the control of a vehicle may be automatically detected by processing images obtained while a vehicle performs maneuvers that are suspected as resulting from situations that are suspected as requiring human intervention in the control of a vehicle.

Situations that are suspected as requiring human intervention in the control of a vehicle may be detected based on behavioral information regarding behavior of the plurality of vehicles, during an execution of the maneuvers that are suspected as resulting from situations that are suspected as requiring human intervention in the control of a vehicle.

The behavioral information may represent the behavior (speed, direction of propagation, and the like) of the entire vehicle and/or may represent the behavior of parts or components of the vehicle (for example damping a shock by a shock absorber, slowing the vehicle by the breaks, turns of the steering wheel, and the like), and/or may represent the behavior of the driver (the manner in which the driver drives), and the like.

After a situation is tagged as requiring human intervention in the control of a vehicle—the vehicle may trigger human intervention when this situation is visually detected. For example—some events (or some types of events) may be defined (even based on previous recognition of a situation as requiring human intervention in the control of a vehicle) as requiring human intervention. For example—an image of a cross road located near a school, an image of a school bus that transports children, an image of a bar and drunken pedestrians near the bar, an image of multiple people that are near the road, and the like may be flagged as situation as requiring human intervention in the control of a vehicle

A determination of what constitutes a vehicle maneuver suspected as requiring human intervention in the control of a vehicle may be detected in a supervised manner or in a unsupervised manner.

Such a maneuver may be an uncommon maneuver in comparison to at least one out of (a) maneuvers of the same vehicle, (b) maneuvers performed at the same location by other drivers, (c) maneuvers performed in other places by other drivers, and the like. The maneuver may be compared to other maneuvers at the same time of day, at other times of day, at the same date, at other dates and the like.

The vehicle maneuver suspected as related to a situation that requires human intervention in the control of a vehicle may involve a change (especially a rapid change) in the direction and/or speed and/or acceleration of the vehicle, a deviation from a lane, a deviation from a previous driving pattern followed at attempt to correct the deviation, and the like.

For example—when applying supervised learning of obstacles—the computerized system may be fed with examples of vehicle behaviors that require human intervention in the control of a vehicle.

For example—the determining may include ruling out common behaviors such as stopping in front of a red traffic light.

For example—a fast and significant change in the speed and direction of a vehicle at a linear road path may indicate that the vehicle encountered a situation requiring human intervention in the control of a vehicle

The visual information may be raw image data (such as images) acquired by a visual sensor of the vehicle, processed images, metadata or any other type of visual information that represents the images acquired by visual sensor.

The visual information may include one or more robust signatures of one or more images acquired by one or more visual sensors of the vehicle. A non-limiting example of a robust signature is illustrated in U.S. Pat. No. 8,326,775 which is incorporated herein by reference.

Step 1456 may be followed by step 1460 of transmitting to one or more of the plurality of vehicles, the trigger visual information.

FIG. 20 illustrates a method 1490 for detecting situation that requires human intervention in the control of a vehicle. The method may be executed by a vehicle.

Method 1490 may start by steps 1491 and 1492.

Step 1491 may include sensing, by a non-visual sensor of a vehicle, a behavior of a vehicle. The non-visual sensor may be an accelerometer, a shock absorber sensor, a brakes sensor, and the like.

Step 1492 may include acquiring, by a visual sensor of the vehicle, images of an environment of the vehicle.

Steps 1491 and 1492 may be followed by step 1493 of determining, by a processing circuitry of the vehicle, whether the behavior of the vehicle is indicative of a vehicle maneuver that is related to a situation that is suspected as requiring human intervention in the control of a vehicle.

The vehicle maneuver related to a situation that is suspected as requiring human intervention in the control of a vehicle may be detected based on behavioral information that may be obtained from one or more sensors such as an accelerometer, a wheel speed sensor, a vehicle speed sensor, a brake sensor, a steering wheel sensor, an engine sensor, a shock absorber sensor, a driver sensor for sensing one or more physiological parameter of the driver (such as heart beat), or any other sensor—especially any other non-visual sensor.

The vehicle maneuver related to a situation that is suspected as requiring human intervention in the control of a vehicle may involve a change (especially a rapid change) in the direction and/or speed and/or acceleration of the vehicle, a deviation from a lane, a deviation from a previous driving pattern followed at attempt to correct the deviation, and the like.

Step 1493 may be followed by step 1494 of processing the images of the environment of the vehicle obtained during a vehicle maneuver related to a situation that is suspected as requiring human intervention in the control of a vehicle to provide visual information.

The visual information may be raw image data (such as images) acquired by a visual sensor of the vehicle, processed images, metadata or any other type of visual information that represents the images acquired by visual sensor.

The visual information may include one or more robust signatures of one or more images acquired by one or more visual sensors of the vehicle. A non-limiting example of a robust signature is illustrated in U.S. Pat. No. 8,326,775 which is incorporated herein by reference.

Step 1494 may be followed by step 1495 of transmitting the visual information to a system that is located outside the vehicle.

Method 1490 may also include step 1496 of receiving at least one visual obstacle identifier from a remote computer.

Method 1490 may include step 1497 receiving verification information indicative of whether the maneuver related to a situation that is suspected as requiring human intervention in the control of a vehicle maneuver is actually related to a situation that actually requires human intervention in the control of a vehicle. This may be used in the vehicle during step 1493.

Methods 1450 and 1490 illustrate a learning process. The products of the learning process may be used to detect situations that require human intervention in the control of a vehicle—during an vehicle control process that uses the outputs of the learning process.

The learning process may continue during the vehicle control process or may terminate before the vehicle control process.

FIG. 21 illustrates method 1470.

Method 1470 may start by step 1472 of receiving, by an I/O module of a vehicle, trigger visual information for visually identifying situations that require human intervention in the control of a vehicle. The visual obstacle identifier is generated based on visual information acquired by at least one visual sensor during an execution of at least vehicle maneuver that is suspected as being an obstacle avoidance maneuver. Thus step 1472 may include receiving outputs of the learning processes of method 1450.

Method 1470 may also include step 1474 of acquiring, by a visual sensor of the vehicle, images of an environment of the vehicle.

Step 1474 may be followed by step 1476 of searching, by a processing circuitry of the vehicle, images of the environment of the vehicle for a situation that is identified by the trigger visual information.

If finding such a situation then step 1476 may be followed by step 1478 of triggering human intervention in the control of a vehicle. This may include alerting the driver that human intervention is required, handing the control of the vehicle to the driver- and the like.

Step 1478 may include, for example—generating an alert perceivable by a human driver of the vehicle, sending an alert to a computerized system located outside the vehicle, handing over the control of the vehicle to a human driver, handing over a control of the vehicle from an autonomous driving manager, sending an alert to an autonomous driving manager, informing another vehicle about the trigger, and the like.

FIGS. 22-27 illustrate example of situations that are suspected as requiring human intervention in the control of a vehicle.

FIG. 22 illustrates first vehicle VH1 1801 as stopping (position 1501) in front of a puddle 1506 and then passing the puddle (may drive straight or change its direction till ending the maneuver at point 1502. The maneuver may be indicative that passing the puddle may require human intervention.

Visual information acquired between positions 1501 and 1502 are processed during step 1494.

FIG. 23 illustrates first vehicle VH1 1801 as sensing pedestrians 1511 and 1512. These pedestrians either are associated with temporal signatures that illustrate their movement (1511′ and 1512′) and/or the vehicle may sense the movements of the pedestrians—and the vehicle may send (based on the movement) the future movement and the future location (1511″ and 1512″) of the pedestrians and perform a maneuver that may include altering speed (for example stopping at point 1513) and/or bypassing the pedestrians (maneuver 1515).

Visual information acquired between positions 1513 and 1514 (end of the maneuver) are processed during step 1494.

FIG. 24 illustrates first vehicle VH1 1801 as sensing parked vehicles PV1 1518 and PV2 1519 that part on both sides of a double-lane bi-directional road, that require the first vehicle to perform a complex maneuver 1520 that includes changing lanes and changing direction relatively rapidly.

Visual information acquired between positions 1516 (beginning of the maneuver) and 1517 (end of the maneuver) are processed during step 1494.

FIG. 25 illustrates first vehicle VH1 1801 as stopping (position 1522) in front of wet segment of the road on which rain 1521 (from cloud 1522) falls. The stop (at location 1522) and any further movement after moving to another part of the road may be regarded as a maneuver 1523 that is indicative that passing the wet segment may require human intervention.

Visual information acquired between position 1522 (beginning of the maneuver) and the end of the maneuver are processed during step 1494.

FIG. 26 illustrates first vehicle VH1 1801 as stopping (position 1534) in front of a situation that may be labeled as a packing or unpacking situation—a track 1531 is parked on the road, there is an open door 1532, and a pedestrian 1533 carries luggage on the road. The first vehicle 1801 bypasses the truck and the pedestrian between locations 1534 and 1535 during maneuver 1539. The maneuver may be indicative that a packing or unpacking situation may require human intervention.

Visual information acquired between positions 1534 and 1535 are processed during step 1494.

FIG. 27 illustrates first vehicle VH1 1801 as turning away (maneuver 1540) from the road when sensing that it faces a second vehicle VH2 1802 that moves towards VH1 1801. Maneuver 1540 may be indicative that such as potential collision situation may require human intervention.

Visual information acquired between positions 1541 and 1542 (start and stop of maneuver 1540) are processed during step 1494.

Tracking after an Entity

There may be beneficial to detect and object in a robust and an efficient manner by taking into account the temporal behavior of the entity and also its spatial signature. Using signatures of multiple type may increase the reliability of the detection, one signature may verify the other signature. In addition—one signature may assist in detecting the entity when the other signature is missing or of a low quality. Furthermore—estimating the future movement of an entity—based on the spatial signature of the entity (or of those of entities of the same type) may enable to predict the future effect that this entity may have on the vehicle and allow to determine the future driving pattern of the vehicle in advance.

The temporal signature may be compresses—thus saving memory and allowing a vehicle to allocate limited memory resources for tracking each entity—even when tracking multiple entities.

FIG. 28 illustrates a method 2600 for tracking after an entity.

Method 2600 may include steps 2602, 2604, 2606 and 2608.

Tracking, by a monitor of a vehicle, a movement of an entity that appears in various images acquired during a tracking period. S2602.

Generating, by a processing circuitry of the vehicle, an entity movement function that represents the movement of the entity during the tracking period. S2604.

Generating, by the processing circuitry of the vehicle, a compressed representation of the entity movement function. S2606.

Responding to the compressed representation of the entity movement function. S2608.

The compressed representation of the entity movement function may be indicative of multiple properties of extremum points of the entity movement function.

The multiple properties of an extremum point of the extremum points, may include a location of the extremum point, and at least one derivative of the extremum point.

The multiple properties of an extremum point of the extremum points may include a location of the extremum point, and at least two derivative of at least two different orders of the extremum point.

The multiple properties of an extremum point of the extremum points, may include a curvature of the function at a vicinity of the extremum point.

The multiple properties of an extremum point of the extremum points, may include a location and a curvature of the function at a vicinity of the extremum point.

Method 2600 may include acquiring the images by a visual sensor of the vehicle. S2601.

At least one image of the various images may be acquired by an image sensor of another vehicle.

Step 2608 may include at least one out of:

Storing, in a memory unit of the vehicle, the compressed representation of the entity movement function. Transmitting the compressed representation of the entity movement function to a system that may be located outside the vehicle. Estimating, by a processing circuitry of the vehicle, a future movement of the entity, based on the compressed representation of the entity movement function. Generating a profile of the entity, by a processing circuitry of the vehicle, based on the compressed representation of the entity movement function. Predicting an effect of a future movement of the entity on a future movement of the vehicle, wherein the predicting may be executed by a processing circuitry of the vehicle, and may be based on the compressed representation of the entity movement function. searching for a certain movement pattern within the movement of the entity, by a processing circuitry of the vehicle, based on the compressed representation of the entity movement function.

Method 2600 may include receiving a compressed representation of another entity movement function, the other entity movement function may be generated by another vehicle and may be indicative of the movement of the entity during at least a subperiod of the tracking period. S2610.

Method 2600 may include amending the compressed representation of the entity movement function based on the compressed representation of the other entity movement function. S2612.

Method 2600 may include determining a duration of the tracking period. S2603. The duration may be determine based on the certainty level of the prediction, based on memory and/or computational resources allocated to the tracking, and the like.

FIG. 29 illustrates examples of entity movement functions. One axis is time. Other axes are spatial axes (x-axis, y-axis and the like).

FIG. 30 illustrates method 2630.

Method 2630 may include steps 2631, 2632, 2634 and 2636.

Method 2630 may include calculating or receiving an entity movement function that represents a movement of the entity during a tracking period. S2632.

Searching, by a search engine, for a matching reference entity movement function. S2634

Identifying the entity using reference identification information that identifies a reference entity that exhibits the matching reference entity movement function. S2636.

The reference identification information may be a signature of the entity.

The method may include acquiring a sequence of images by an image sensor; and calculating the entity movement based on the sequence of images. S2631

FIG. 31 illustrates a method 2640.

Method 2630 may include steps 2642, 2644, 2646 and 2648.

Method 2640 may start by calculating or receiving multiple entity movement functions that represent movements of multiple entities. S2642.

Clustering the multiple entity movement functions to clusters. S2644.

For each cluster, searching, by a search engine, for a matching type of reference entity movement functions. S2646.

Identifying, for each cluster, a type of entity, using reference identification information that identifies a type of reference entities that exhibits the matching type of reference entity movement functions. S2648.

FIG. 32 illustrates a method 2650.

Method 2650 may include steps 2652, 2654 and 2656.

Method 2650 may start by calculating or receiving (a) an entity movement function that represents a movement of an entity, and (b) a visual signature of the entity. S2652.

Comparing the entity movement function and the visual signature to reference entity movement functions and reference visual signatures of multiple reference objects to provide comparison results. S2654.

Classifying the object as one of the reference objects, based on the comparison results. S2656.

FIG. 33 illustrates a method 2660.

Method 2660 may include steps 2662, 2664, 2666 and 2668.

Step 2662 may include calculating or receiving an entity movement function that represents a movement of an entity.

Step 2664 may include comparing the entity movement function to reference entity movement functions to provide comparison results.

Step 2666 may include classifying the object as a selected reference object of the reference objects, based on the comparison results.

Step 2668 may include verifying the classifying of the object as the selected reference object by comparing a visual signature of the object to a reference visual signature of the reference object.

FIG. 34 illustrates a method 2670.

Method 2670 may include steps 2672, 2674, 2676 and 2678.

Step 2672 may include calculating or receiving a visual signature of the object.

Step 2674 may include comparing a visual signature of the object to reference visual signatures of multiple reference objects to provide comparison results.

Step 2644 may include classifying the object as a selected reference object of the reference objects, based on the comparison results.

Step 2678 may include verifying the classifying of the object as the selected reference object by comparing an entity movement function that represents a movement of the entity to a reference entity movement functions to provide comparison results.

FIG. 35 illustrates a method 2680.

Method 2680 may include steps 2682, 2684 and 2686.

Method 2680 is for generating a signature of an object.

Step 2682 may include calculating or receiving a visual signature of the object.

Step 2684 may include calculating or receiving an entity movement function that represents a movement of the object.

Step 2686 may include generating a spatial-temporal signature of the object that represents the visual signature and the entity movement function of the object.

FIG. 36 illustrates a method 2700.

Method 2700 may include steps 2702, 2704 and 2706.

Method 2700 is for driving a first vehicle based on information received from a second vehicle.

The exchange of information—especially compact and robust signatures between vehicles may improve the driving of the vehicle and at a modest cost—especially when the vehicles exchange compact signatures between themselves.

Step 2702 may include receiving, by the first vehicle, acquired image information regarding (a) a signature of an acquired image that was acquired by the second vehicle, (b) a location of acquisition of the acquired image.

Step 2704 may include extracting, from the acquired image information, information about objects within the acquired image.

Step 2706 may include preforming a driving related operation of the first vehicle based on the information about objects within the acquired image.

The acquired image information regarding the robust signature of the acquired image may be the robust signature of the acquired image.

The acquired image information regarding the robust signature of the acquired image may be a cortex representation of the signature.

The method may include acquiring first vehicle images by the first vehicle; extracting, from the first vehicle acquired images, information about objects within the first vehicle acquired images; and preforming the driving related operation of the first vehicle based on the information about objects within the acquired image and based on the information about objects within the first vehicle acquired images.

The image information may represent data regarding neurons of a neural network, of the second vehicle, that fired when the neural network was fed with the acquired image.

The extracting, of the information about objects within the acquired image may include (a) comparing the signature of the acquired image to concept signatures to provide comparison results; each concept signature represents a type of objects; and (b) determining types of objects that may be included in the acquired image based on the comparison results.

FIG. 37 illustrates one vehicle that updates another vehicle—especially with objects that are not currently seen by the other vehicle—thereby allowing the other vehicle to calculate his driving path based (also) on the object it currently does not see.

Concept Update

A concept structure includes multiple signatures that are related to each other and metadata related to these signatures.

The concepts may be updated by removing signatures—and even parts of the signatures that are more “costly” to keep than maintain.

The “cost” may represent one or more factors such as false detection probability, a robustness of the concept, an accuracy of the concept, and the like.

One or more vehicles may decide to update a concept—and then send the update (or an indication about the update) to other vehicles—therefore improving the detection process by using updated concepts.

FIG. 38 illustrates a method 2710.

Method 2710 may include steps 2712, 2714 and 2726.

Method 2710 is for concept update.

Method 2710 may include:

Step 2712 of detecting that a certain signature of an object causes a false detection (see, for example FIGS. 39 and 40). The certain signature belongs to a certain concept structure that may include multiple signatures. The false detection may include determining that the object may be represented by the certain concept structure while the object may be of a certain type that may be not related to the certain concept structure. For example—a concept of a pedestrian may classify (by error) a mail box as a pedestrian. Step 2714 of searching for an error inducing part (see, for example FIGS. 41-43) of the certain signature that induced the false detection. Step 2715 of determining whether to remove the error inducing part of the certain signature. This may involve calculating a cost related to a removing the error inducing part from the concept structure—and removing The error inducing part when the cost may be within a predefined range. Step 2726 of removing (see, for example FIG. 44) from the concept structure the error inducing part to provide an updated concept structure.

Each signature may represent a map of firing neurons of a neural network.

Step 2714 may include:

Generating or receiving a test concept structure that includes (a) first signatures of images that may include one or more objects of the certain type (that should not belong to the concept), and (b) second signatures of second images that may include one or more objects of a given type that may be properly associated with the concept structure. The images of both types may be selected in any manner—for example may be randomly selected. Comparing the certain signature to the test concept structure to provide matching first signatures and matching second signatures. Comparing the matching first signatures and matching second signatures to find parts that causes false errors and parts that result in positive detection. Defining the error inducing part of the certain signature based on an overlap between the matching parts of the certain signature.

The updated concept may be shared between vehicles.

Determination of Score Driver Behavior

Human drivers' behavior and driving skills are in many cases cause accidents or lead to dangerous situations. For example, it is not uncommon for a driver not to stop at a stop sign, cross the crossing in a red light, cross a solid white line etc—situations that endanger the surrounding road users. Unless a police officer is around, these violations go unnoticed

Fallen Cargo Detection

Fallen cargo is the reason of many accidents. A fallen cargo item may damage a car while falling from a vehicle, after falling from a vehicle, may cause another vehicle to change its trajectory and hit yet a further vehicle, and the like.

The detection of fallen cargo may be more problematic than the detection of vehicle for various reasons including, for example, (a) a wider variety of classes of fallen cargo (in comparison to the variety of vehicle), and (b) the classification of an object as a fallen cargo may depend on the object itself and on additional factors such as a location of the object (on the road, conveyed by a vehicle, positioned outside the road).

The impact of a fallen cargo on a vehicle driving over the fallen cargo or near the fallen cargo may also vary from having no effect to crashing the vehicle.

FIG. 45 illustrates method 100 for detecting fallen cargo.

Method 100 may start by step 110 of receiving by a computerized system, sensed information related to driving sessions of multiple vehicles.

The computerized system may be a remote system in the sense that is not located in any of the multiple vehicle. Alternatively, the computerized system may be implemented in at least one of the vehicles.

Step 110 may be followed by step 120 of applying a machine learning process on the sensed information to detect fallen cargo and to classify the fallen cargo to fallen cargo classes.

The fallen cargo classified to the different fallen cargo classes may differ from each other by size, shape, material, rigidness, type, color, location in the path, and may include, for example, barb wires, pipes, furniture, trash, bars, barbed wire, concrete blocks, duct pipes, bins, lids of bins, bottles, containers, boxes, electronic devices, organic objects, non-organic objects, metal objects, wooden objects, sharp objects, smooth objects, fences, rods, coils.

The fallen cargo classes may be defined in one or more resolutions. The fallen cargo classes may be defined based on the type of carried object and/or on a property (such as size, shape, material, type, color, location in the path) of the objects.

For example, regarding object based classification—coarse resolution classes may include, for example livestock, furniture, wood, raw materials, vehicles, and the like. Finer resolution classes may include chicken, cows, horses, pigs, turkey, timbers, chairs, tables, cupboard, pipes, barb wire, containers, boxes, electronic devices, bins. Even more finer classes may include a combination of an object and a property (for example timbers of certain size ranges will belong to different classes).

During the training process the classes of the object may be tagged, may be learnt by any object detection process, and the like.

The classification can be performed in various manners—for example, during the machine learning process the fallen cargo may be tagged as belonging to certain classes, merely tagged as “fallen cargo” without tagging it to classes, may be tagged as belonging to a group of classes, and the like.

It should be noted that during the machine learning process different images may be tagged with tags of different resolution—for example—one image may be tagged as “fallen cargo”, another may be tagged as “fallen chair”, and the like.

A tagging of a sensed information (for example—an image) may include associating information related to the content of at least a part of the image with the content. The tagging may indicate the location of the object within the image.

Step 120 may be followed by step 130 of estimating, from the sensed information, an impact of at least some of the fallen cargo classes on a behavior of at least some of the multiple vehicles.

The sensed information may include information about how the vehicles responded to the fallen cargo. The sensed information may include, for example, video streams that represent how the vehicles drive in the presence of the fallen cargo.

The sensed information may include videos of vehicles that drove around, over or otherwise at the vicinity of the cargo. The video may be associated with information (for example—tagged) regarding damages that occurred to vehicles due to the cargo, a hazard parameter associated with the fallen cargo, and the like.

A machine learning process may be trained with such video streams or other sensed information representing the response of said vehicles to fallen cargo. Following the training the machine learning process may be configured to detect an impact of each class of fallen cargo on a vehicle. The machine learning process may be the same machine learning process of step 120.

The estimation may include tracking the behavior of vehicle at the presence of the fallen cargo. The behavior may be reflected in the sensed information (for example drivers stopping the vehicle before the fallen cargo, bypassing the fallen cargo, changing direction and/or speed at the presence of the fallen cargo, and the like). The estimation of the impact may be also learnt from other source of information such as damage reports, information regarding the fallen cargo that was spread through the social media, and the like.

The training process may be executed in a supervised or non-supervised manner.

Step 130 of estimating may be based at least in part on motion estimation of the vehicle. The motion estimation represents how the drivers responded to the fallen cargo. The motion estimation can be based on visual sensed information and/or on non-visual sensed information—such as acceleration, speed, and the like.

Step 130 of estimating may be based at least in part on sensed information sensed by the least one vehicle sensor—such as a speedometer, accelerator, vibration sensor, and the like.

Step 130 of estimating may include estimating a level of danger associated with at least some of the fallen cargo classes.

The estimating of the level of danger may be responsive to a rate of deviation of the vehicles from a previous propagation direction. For example—making sharper turns at higher speed may be deemed to be more dangerous than making milder turns at lower speed.

The estimating of the level of danger can be done automatically via monitoring the telematics parameters of the vehicle that drives in the presence of the fallen cargo.

The estimating of the level of danger can be done automatically via monitoring the sensed video of the vehicle that drives in the presence of the fallen cargo.

The method may include generating a profile of a driver that drives the vehicle that should perform the fallen cargo maneuver, or at least estimating some driver capabilities such as general skill, skill in relation to certain maneuvers. Step 130 of estimating of the level of danger may be responsive to driver profile and/or driver parameters. The estimating may involve processing sensed information that was sensed by a specific driver. The profile of the driver may be used to normalize (or at least take into account) the behavior of the driver at the presence of the fallen cargo. For example—lower weight (when determining the danger level) may be assigned to a sharp turn of a wild driver at the presence of the fallen cargo. Alternatively—the driver profile is not taken into account.

Step 130 may be followed by step 140 of determining, based on the impact, at least one suggested vehicle behavior as a response to a detection of at least some of the fallen cargo classes.

The determining may be executed as part of the machine learning process and/or may be executed in any other manner.

The suggested vehicle behavior may be determined in various manners—for example it may be determined to optimize vehicle behavior in relation to the safety of the vehicle, may be determined based on one or more other parameters, may be determined in a sub-optimal manner in relation to the safety. The one or more other parameters may be fuel consumption, deterioration of brakes, a time of overcoming the fallen cargo, an affect on the pattern on traffic, and the like.

Step 140 may include generation of a driver perceivable alert when a fallen cargo is detected. This may be an audio and/or visual alert.

Step 140 may be followed by step 150 of responding to the determining.

Step 150 may include distributing to various vehicles the suggested vehicle behaviors as a response to a detection of at least some of the fallen cargo classes. The suggested behaviors may be suggested to a driver (during driver controlled driving of the vehicle) when fallen cargo of certain classes are detected, and/or may be implemented by a vehicle (during autonomous driving) when a fallen cargo of certain classes are detected.

FIGS. 46-62 illustrates various responses of vehicles to different classes of fallen cargo. The responses include passing over fallen cargo, bypassing the fallen cargo with or without stopping or slowing down, crossing another lane, exiting (at least in part) the road, turning back, and the like.

The different classes of fallen cargo illustrated in these figures are merely non-limiting example of fallen cargo and include a lid 201 of a bin, and pipes 202. Visual information (or other sensed information) is acquired by vehicle 10 that may respond in different manners to the different fallen cargo. The sensed information is sent from vehicle 10 to computerized system 300 located outside the vehicle. Examples of vehicle and computerized systems are illustrated in PCT/1132019/058207 filing date Sep. 27, 2019 which is incorporated herein by reference.

The vehicle is illustrated as including one or more vehicle sensors 11, communication module 12, processor 13, memory unit 14. The processor 13 may executed code for performing autonomous driving and/or driver assistance.

The processor may include any of the components illustrated in FIGS. 1A-2N.

The computerized system 300 may include communication module 302, processor 303, memory unit 304, and may generate responses for coping with different classes of fallen cargo.

Non-limiting examples of vehicles and computerized systems are illustrated in PCT/1132019/058207 filing date Sep. 27, 2019 which is incorporated herein by reference. Any method, device or computer readable medium for detecting obstacles and/or for responding to obstacles illustrated in PCT/1132019/058207 filing date Sep. 27, 2019 may be applied mutatis mutandis to driving at the presence of the fallen cargo.

FIG. 61 illustrates method 300 of estimating a future fall of a cargo conveyed by a monitored vehicle. The monitored vehicle is a vehicle that is monitored by another vehicle or by any other monitoring entity.

Method 300 may start by step 310 of receiving, by a monitoring vehicle, one or more future falling cargo predictors for multiple classes of cargo that were learnt by applying a machine learning process. A cargo class may be associated with one or more future falling cargo predictors.

The future falling cargo classes (also referred to as cargo class) may be the same as the fallen cargo classes or may differ from the fallen cargo classes.

The future falling cargo classified to the different future falling cargo classes may differ from each other by size, shape, material, rigidness, type, color, location in the path, and may include, for example, barb wires, pipes, furniture, trash, bars, barbed wire, concrete blocks, duct pipes, bins, lids of bins, bottles, containers, boxes, electronic devices, organic objects, non-organic objects, metal objects, wooden objects, sharp objects, smooth objects, fences, rods, coils.

The future falling cargo classes may be defined in one or more resolutions. The future falling cargo classes may be defined based on the type of carried object and/or on a property (such as size, shape, material, type, color, location in the path) of the objects.

For example, regarding object based classification—coarse resolution classes may include, for example livestock, furniture, wood, raw materials, vehicles, and the like. Finer resolution classes may include chicken, cows, horses, pigs, turkey, timbers, chairs, tables, cupboard, pipes, barb wire, containers, boxes, electronic devices, bins. Even more finer classes may include a combination of an object and a property (for example timbers of certain size ranges will belong to different classes).

During the training process the classes of the object may be tagged, may be learnt by any object detection process, and the like.

The training process may be executed in a supervised or unsupervised manner, whereas the tagging can be made in various resolutions—as illustrated, for example, in relation to method 100 and/or method 400.

The one or more future falling cargo predictors may be visual predictors, textual predictors, audio predictors or a combination thereof. Non-limiting examples of future falling cargo predictors include fastening element faults (such as a misplaced, a missing or a torn fastening belt), a loose cargo item, a movement of the cargo item towards an exterior of the monitored vehicle, imbalance of the vehicle conveying the cargo, overweight cargo, and the like.

The one or more future falling cargo predictors may be a combination of one or more features—and may be taught by the machine learning process without defining a textual rule.

The one or more future falling cargo predictors may provide information about a probability of falling, a timing of the falling, the trajectory of the cargo during the falling and after the falling, and the like.

The one or more future falling cargo predictors may be learnt, for example, by applying method 400.

The machine learning process may be executed in any manner illustrated in FIGS. 1A-2N, and/or in any manner illustrated in PCT/IB2019/058207 filing date Sep. 27, 2019 which is incorporated herein by reference. Any method, device or computer readable medium for detecting obstacles and/or for responding to obstacles illustrated in PCT/IB2019/058207 filing date Sep. 27, 2019 may be applied mutatis mutandis to estimating a future fall of a cargo conveyed by a monitored vehicle.

Step 310 may be followed by step 320 of sensing by at least one sensor the monitoring vehicle to provide monitoring results.

Step 320 may be followed by step 330 of determining a cargo class of the cargo carried by the monitored vehicle.

Step 330 may be followed by step 340 of estimating, based on the cargo class and a future falling cargo predictor associated to the cargo class, whether the cargo conveyed by the monitored vehicle will fall before the monitoring vehicle will pass the monitored vehicle.

Cargo that fall after the passage of the vehicle may be irrelevant or lesser significance.

Step 340 may be followed by step 350 of responding to the estimating.

The responding may include various responses such as but not limited to:

-   -   Providing a falling cargo indication. The indication may be         audio, may be visual, may be audio-visual, may be a human         perceivable indication, and the like.     -   Amending a behavior of the monitoring vehicle. This may include,         for example, changing the driving patters, for example lower         speed, decelerating, and the like.     -   Suggesting a suggested falling cargo avoidance driving pattern.         This may include bypassing the falling cargo. The suggested         falling cargo avoidance driving pattern may be tailored to the         class of the cargo.

A future falling cargo predictor may be associated with a cargo class and may be indicative of a behavior of a vehicle conveying the cargo that contributes to a future falling of cargo. Step 340 may be based, at least in part, on a behavior of the monitored vehicle. The behavior may refer to the driving pattern of the vehicle (speeding, taking sharp turns, accelerating, driving outside the road), and/or to the manner that the cargo is fastened to the vehicle or carried by the vehicle.

A future falling cargo predictor associated with the cargo class may be indicative of an attribute of a road that contributes to a future falling of cargo. Step 340 may be based, at least in part, on a road over which the monitored vehicle is driving or is about to drive. Road attributed may include smoothness, road damages, grip level, road conditions (icy, wet), the inclusions of obstacles, and the like.

FIG. 62 illustrates an example of method 400 for detecting fallen cargo.

Method 400 may start by step 410 of receiving by a computerized system, sensed information. The sensed information may be related to driving sessions of multiple vehicles.

Step 410 may be followed by step 420 of applying a machine learning process on the sensed information to detect actual or estimated cargo falling events and generate one or more future falling cargo predictors for multiple classes of cargo.

The machine learning process may be a supervised machine learning or an unsupervised machine learning process.

Step 420 may include training the machine learning process with samples of cargos of the multiple classes and with samples of cases where the cargo of the multiple classes fell and where cargo did not fell.

When the sensed information describes (a) an event where the cargo fell (the occurrence of the event may be tagger or may be automatically detected), and (b) the a preceding time period (that preceded the event) during which the cargo was about to fall—then the sensed information obtained during the preceding time period may be analyzed (for example by machine learning) to find the one or more future falling cargo predictors.

For example—assuming that the sensed information is a video stream—then once detecting a falling event in a group of video frames—the frames before that group are analyzed to file the one or more future falling cargo predictors.

The sensed information regarding the cargo falling events may be compared to sensed information about cargo that was conveyed by a vehicle and did not fell.

The machine learning process may be applied on (a) first sensed information that represents falling cargo events, and on (b) second sensed information that represents cargo that did not fall from the vehicle. The first and second sensed information may be tagged as first and second sensed information—but this is not necessarily so.

The learning process may be based on sensed information regarding only cargo that fell, or both sensed information about cargo that fell and cargo that did not fall. The differences between the sensed information may improve the determination of the reasons of the fall.

The differences between cargo that fell and cargo that did not fell may be more accurate if done after the cargo was classified.

The classification can be performed in various manners—for example, during the machine learning process the future falling cargo may be tagged as belonging to certain classes, merely tagged as “future falling cargo” without tagging it to classes, may be tagged as belonging to a group of classes, and the like.

It should be noted that during the machine learning process different images may be tagged with tags of different resolution—for example—one image may be tagged as “future falling cargo”, another may be tagged as “future falling chair”, and the like.

A tagging of a sensed information (for example—an image) may include associating information related to the content of at least a part of the image with the content. The tagging may indicate the location of the object within the image.

Step 420 may include identifying spatial relationship between the cargo of the different classes and vehicles conveying the cargo of the different classes and an outcome of the conveying of the cargo of the multiple classes. For example—the spatial relationship may include a deviation of at least a part of the cargo from the vehicle.

Step 420 may be followed by step 430 of estimating, from the sensed information, an impact of cargo falling events related to at least some of the cargo classes.

The sensed information may include videos of vehicles that drove around, over or otherwise at the vicinity of the future falling cargo. The video may be associated with information (for example—tagged) regarding damages that occurred to vehicles due to the future falling cargo, a hazard parameter associated with the future falling cargo, and the like.

Step 430 may be followed by step 440 of responding to the estimating. The responding may include at least one out of (a) storing the one or more future falling cargo predictors for the multiple classes of cargo, (b) transmitting the one or more future falling cargo predictors for the multiple classes of cargo; (c) storing the estimated impact of cargo falling events related to the at least some of the classes of cargo, and (d) transmitting the impact of cargo falling events related to the at least some of the classes of cargo.

The responding may include determining suggested vehicle behaviors in response to cargo falling events related to the at least some of the classes of cargo.

Method 400 may include detecting a behavior of a vehicle conveying a cargo of a certain class that contributes to the falling of cargo of the certain class; and associating an indication of the behavior with a future falling cargo predictor for the certain cargo class.

FIG. 63 illustrates an image acquired by a monitoring vehicle (not shown) of three monitored vehicles—such as first truck 271, second truck 272 and third truck 273.

The first truck 271 is leveled, all the pipes carried by the first truck are tightly fastened to the first truck without exceeding the borders of the first truck and can be regarded as cargo that will not fall—at least until the monitoring vehicle will pass the first truck.

The second and third trucks 272 and 273 impose a risk—and their cargo is expected to fall—due to imbalance of the third truck 273, or due to loose pipes—some of which exceed the border of the second truck 272.

A falling cargo event may be detected by processing sensed information. Once a falling cargo event is detected the sensed information that precedes the falling event may be processed to find predictors for falling. This detection may be executed without tagging the sensed information and/or following a completion of the machine learning process.

Any reference in the specification to a method should be applied mutatis mutandis to a system capable of executing the method and should be applied mutatis mutandis to a non-transitory computer readable medium that stores instructions that once executed by a computer result in the execution of the method.

Any reference in the specification to a system and any other component should be applied mutatis mutandis to a method that may be executed by a system and should be applied mutatis mutandis to a non-transitory computer readable medium that stores instructions that may be executed by the system.

Any reference in the specification to a non-transitory computer readable medium should be applied mutatis mutandis to a system capable of executing the instructions stored in the non-transitory computer readable medium and should be applied mutatis mutandis to method that may be executed by a computer that reads the instructions stored in the non-transitory computer readable medium.

Any combination of any module or unit listed in any of the figures, any part of the specification and/or any claims may be provided. Especially any combination of any claimed feature may be provided.

Any reference to the term “comprising” or “having” should be interpreted also as referring to “consisting” of “essentially consisting of”. For example—a method that comprises certain steps can include additional steps, can be limited to the certain steps or may include additional steps that do not materially affect the basic and novel characteristics of the method—respectively.

The invention may also be implemented in a computer program for running on a computer system, at least including code portions for performing steps of a method according to the invention when run on a programmable apparatus, such as a computer system or enabling a programmable apparatus to perform functions of a device or system according to the invention. The computer program may cause the storage system to allocate disk drives to disk drive groups.

A computer program is a list of instructions such as a particular application program and/or an operating system. The computer program may for instance include one or more of: a subroutine, a function, a procedure, an object method, an object implementation, an executable application, an applet, a servlet, a source code, an object code, a shared library/dynamic load library and/or other sequence of instructions designed for execution on a computer system.

The computer program may be stored internally on a computer program product such as non-transitory non-transitory computer readable medium. All or some of the computer program may be provided on non-transitory computer readable media permanently, removably or remotely coupled to an information processing system. The non-transitory computer readable media may include, for example and without limitation, any number of the following: magnetic storage media including disk and tape storage media; optical storage media such as compact disk media (e.g., CD-ROM, CD-R, etc.) and digital video disk storage media; nonvolatile memory storage media including semiconductor-based memory units such as FLASH memory, EEPROM, EPROM, ROM; ferromagnetic digital memories; MRAM; volatile storage media including registers, buffers or caches, main memory, RAM, etc. A computer process typically includes an executing (running) program or portion of a program, current program values and state information, and the resources used by the operating system to manage the execution of the process. An operating system (OS) is the software that manages the sharing of the resources of a computer and provides programmers with an interface used to access those resources. An operating system processes system data and user input, and responds by allocating and managing tasks and internal system resources as a service to users and programs of the system. The computer system may for instance include at least one processing unit, associated memory and a number of input/output (I/O) devices. When executing the computer program, the computer system processes information according to the computer program and produces resultant output information via I/O devices.

In the foregoing specification, the invention has been described with reference to specific examples of embodiments of the invention. It will, however, be evident that various modifications and changes may be made therein without departing from the broader spirit and scope of the invention as set forth in the appended claims.

Moreover, the terms “front,” “back,” “top,” “bottom,” “over,” “under” and the like in the description and in the claims, if any, are used for descriptive purposes and not necessarily for describing permanent relative positions. It is understood that the terms so used are interchangeable under appropriate circumstances such that the embodiments of the invention described herein are, for example, capable of operation in other orientations than those illustrated or otherwise described herein.

Those skilled in the art will recognize that the boundaries between logic blocks are merely illustrative and that alternative embodiments may merge logic blocks or circuit elements or impose an alternate decomposition of functionality upon various logic blocks or circuit elements. Thus, it is to be understood that the architectures depicted herein are merely exemplary, and that in fact many other architectures may be implemented which achieve the same functionality.

Any arrangement of components to achieve the same functionality is effectively “associated” such that the desired functionality is achieved. Hence, any two components herein combined to achieve a particular functionality may be seen as “associated with” each other such that the desired functionality is achieved, irrespective of architectures or intermedial components. Likewise, any two components so associated can also be viewed as being “operably connected,” or “operably coupled,” to each other to achieve the desired functionality.

Furthermore, those skilled in the art will recognize that boundaries between the above described operations merely illustrative. The multiple operations may be combined into a single operation, a single operation may be distributed in additional operations and operations may be executed at least partially overlapping in time. Moreover, alternative embodiments may include multiple instances of a particular operation, and the order of operations may be altered in various other embodiments. Also for example, in one embodiment, the illustrated examples may be implemented as circuitry located on a single integrated circuit or within a same device. Alternatively, the examples may be implemented as any number of separate integrated circuits or separate devices interconnected with each other in a suitable manner.

Also for example, the examples, or portions thereof, may implemented as soft or code representations of physical circuitry or of logical representations convertible into physical circuitry, such as in a hardware description language of any appropriate type.

Also, the invention is not limited to physical devices or units implemented in non-programmable hardware but can also be applied in programmable devices or units able to perform the desired device functions by operating in accordance with suitable program code, such as mainframes, minicomputers, servers, workstations, personal computers, notepads, personal digital assistants, electronic games, automotive and other embedded systems, cell phones and various other wireless devices, commonly denoted in this application as ‘computer systems’.

However, other modifications, variations and alternatives are also possible. The specifications and drawings are, accordingly, to be regarded in an illustrative rather than in a restrictive sense.

In the claims, any reference signs placed between parentheses shall not be construed as limiting the claim. The word ‘comprising’ does not exclude the presence of other elements or steps then those listed in a claim. Furthermore, the terms “a” or “an,” as used herein, are defined as one or more than one. Also, the use of introductory phrases such as “at least one” and “one or more” in the claims should not be construed to imply that the introduction of another claim element by the indefinite articles “a” or “an” limits any particular claim containing such introduced claim element to inventions containing only one such element, even when the same claim includes the introductory phrases “one or more” or “at least one” and indefinite articles such as “a” or “an.” The same holds true for the use of definite articles. Unless stated otherwise, terms such as “first” and “second” are used to arbitrarily distinguish between the elements such terms describe. Thus, these terms are not necessarily intended to indicate temporal or other prioritization of such elements. The mere fact that certain measures are recited in mutually different claims does not indicate that a combination of these measures cannot be used to advantage.

While certain features of the invention have been illustrated and described herein, many modifications, substitutions, changes, and equivalents will now occur to those of ordinary skill in the art. It is, therefore, to be understood that the appended claims are intended to cover all such modifications and changes as fall within the true spirit of the invention. 

We claim:
 1. A method for estimating a future fall of a cargo, the method comprises: receiving by a computerized system, sensed information related to driving sessions of multiple vehicles; applying a machine learning process on the sensed information to detect actual or estimated cargo falling events and generate one or more future falling cargo predictors for multiple types of cargo; estimating, from the sensed information, an impact of cargo falling events related to at least some of the types of cargo; and responding to the estimating, wherein the responding comprises at least one out of (a) storing the one or more future falling cargo predictors for the multiple types of cargo, (b) transmitting the one or more future falling cargo predictors for the multiple types of cargo; (c) storing the estimated impact of cargo falling events related to the at least some of the types of cargo, and (d) transmitting the impact of cargo falling events related to the at least some of the types of cargo.
 2. The method according to claim 1, comprising determining suggested vehicle behavior in response to cargo falling events related to the at least some of the types of cargo.
 3. The method according to claim 1, comprising detecting a behavior of a vehicle conveying a cargo of a certain type that contributes to the falling of cargo of the certain type; and associating an indication of the behavior with a future falling cargo predictor for the certain cargo class.
 4. The method according to claim 1, wherein the machine learning process is a supervised machine learning process.
 5. The method according to claim 1, wherein the machine learning process is a unsupervised machine learning process.
 6. The method according to claim 1, wherein the applying of the machine learning process comprises training the machine learning process with samples of cargos of the multiple types and with samples of cases where the cargo of the multiple types fell.
 7. The method according to claim 1, wherein the applying of the machine learning process comprises identifying spatial relationship between the cargo of the different types and vehicles conveying the cargo of the different types and an outcome of the conveying of the cargo of the multiple types.
 8. A non-transitory computer readable medium for detecting fallen cargo, the non-transitory computer readable medium stores instructions for: receiving by a computerized system, sensed information related to driving sessions of multiple vehicles; applying a machine learning process on the sensed information to detect actual or estimated cargo falling events and generate one or more future falling cargo predictors for multiple types of cargo; estimating, from the sensed information, an impact of cargo falling events related to at least some of the types of cargo; and responding to the estimating, wherein the responding comprises at least one out of (a) storing the one or more future falling cargo predictors for the multiple types of cargo, (b) transmitting the one or more future falling cargo predictors for the multiple types of cargo; (c) storing the estimated impact of cargo falling events related to the at least some of the types of cargo, and (d) transmitting the impact of cargo falling events related to the at least some of the types of cargo.
 9. The non-transitory computer readable medium according to claim 8, that stores instructions for determining suggested vehicle behavior in response to cargo falling events related to the at least some of the types of cargo.
 10. The non-transitory computer readable medium according to claim 8, that stores instructions for detecting a behavior of a vehicle conveying a cargo of a certain type that contributes to the falling of cargo of the certain type; and associating an indication of the behavior with a future falling cargo predictor for the certain cargo class.
 11. The non-transitory computer readable medium according to claim 8, wherein the machine learning process is a supervised machine learning process.
 12. The non-transitory computer readable medium according to claim 8, wherein the machine learning process is a unsupervised machine learning process.
 13. The non-transitory computer readable medium according to claim 8, wherein the applying of the machine learning process comprises training the machine learning process with samples of cargos of the multiple types and with samples of cases where the cargo of the multiple types fell.
 14. The non-transitory computer readable medium according to claim 8, wherein the applying of the machine learning process comprises identifying spatial relationship between the cargo of the different types and vehicles conveying the cargo of the different types and an outcome of the conveying of the cargo of the multiple types.
 15. A method for estimating a future fall of a cargo conveyed by a monitored vehicle, the method comprises: receiving, by a monitoring vehicle, one or more future falling cargo predictors for multiple types of cargo that were learnt by applying a machine learning process; sensing by at least one sensor the monitoring vehicle to provide monitoring results; determining a cargo class carried by the monitored vehicle; estimating, based on the cargo class and a future falling cargo predictor associated to the cargo class, whether the cargo conveyed by the monitored vehicle will fall before the monitoring vehicle will pass the monitored vehicle; and responding to the estimating, wherein the responding comprises at least one out of (a) providing a falling cargo indication, (b) amending a behavior of the monitoring vehicle, and (c) suggesting a suggested falling cargo avoidance driving pattern.
 16. The method according to claim 15, wherein a future falling cargo predictors associated with the cargo class is indicative of a behavior of a vehicle conveying the cargo that contributes to a future falling of cargo; and wherein the estimating is based, at least in part, on a behavior of the monitored vehicle.
 17. The method according to claim 15, wherein the future falling cargo predictor associated with the cargo class is indicative of an attribute of a road that contributes to a future falling of cargo; and wherein the estimating is based, at least in part, on a road over which the monitored vehicle is driving or is about to drive.
 18. A non-transitory computer readable medium for estimating a future fall of a cargo conveyed by a monitored vehicle, the non-transitory computer readable medium stores instructions for: receiving, by a monitoring vehicle, one or more future falling cargo predictors for multiple types of cargo that were learnt by applying a machine learning process; sensing by at least one sensor the monitoring vehicle to provide monitoring results; determining a cargo class carried by the monitored vehicle; estimating, based on the cargo class and a future falling cargo predictor associated to the cargo class, whether the cargo conveyed by the monitored vehicle will fall before the monitoring vehicle will pass the monitored vehicle; and responding to the estimating, wherein the responding comprises at least one out of (a) providing a falling cargo indication, (b) amending a behavior of the monitoring vehicle, and (c) suggesting a suggested falling cargo avoidance driving pattern.
 19. The non-transitory computer readable medium according to claim 15, wherein a future falling cargo predictors associated with the cargo class is indicative of a behavior of a vehicle conveying the cargo that contributes to a future falling of cargo; and wherein the estimating is based, at least in part, on a behavior of the monitored vehicle.
 20. The non-transitory computer readable medium according to claim 15, wherein the future falling cargo predictor associated with the cargo class is indicative of an attribute of a road that contributes to a future falling of cargo; and wherein the estimating is based, at least in part, on a road over which the monitored vehicle is driving or is about to drive. 